From 67b2b92690ebce4f8d8ea32fe2337971cfb1cfb2 Mon Sep 17 00:00:00 2001
From: Salim <adsalim007@gmail.com>
Date: Thu, 27 Feb 2025 20:38:03 +0100
Subject: [PATCH 1/3] init commit - adding masking project

---
 gliner/modeling/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gliner/modeling/base.py b/gliner/modeling/base.py
index bcd04cdc..7013a550 100644
--- a/gliner/modeling/base.py
+++ b/gliner/modeling/base.py
@@ -266,7 +266,7 @@ def loss(self, scores, labels, prompts_embedding_mask, mask_label,
         labels = labels.view(-1, num_classes)
         
         all_losses = self._loss(scores, labels, alpha, gamma, label_smoothing)
-
+        #print
         masked_loss = all_losses.view(batch_size, -1, num_classes) * prompts_embedding_mask.unsqueeze(1)
         all_losses = masked_loss.view(-1, num_classes)
 

From 8fca479be93356b15c26e473a003acbdeecc2965 Mon Sep 17 00:00:00 2001
From: Salim <adsalim007@gmail.com>
Date: Thu, 27 Feb 2025 22:18:38 +0100
Subject: [PATCH 2/3] Adding an option to mask spans in loss computation

---
 configs/config.yaml     |    2 +
 examples/finetune.ipynb | 1897 ++++++++++++++++++++-------------------
 gliner/config.py        |    2 +
 gliner/modeling/base.py |   41 +-
 4 files changed, 992 insertions(+), 950 deletions(-)

diff --git a/configs/config.yaml b/configs/config.yaml
index d1456c9e..006f943f 100644
--- a/configs/config.yaml
+++ b/configs/config.yaml
@@ -23,6 +23,8 @@ loss_alpha: 0.75
 loss_gamma: 0
 label_smoothing: 0
 loss_reduction: "sum"
+negative_rate: 0.75
+neg_span_masking : "global_w_threshold"
 
 # Learning Rate and weight decay Configuration
 lr_encoder: 1e-5
diff --git a/examples/finetune.ipynb b/examples/finetune.ipynb
index f6e4fbd1..9d5b70ed 100644
--- a/examples/finetune.ipynb
+++ b/examples/finetune.ipynb
@@ -4,16 +4,16 @@
       "cell_type": "code",
       "execution_count": 1,
       "metadata": {
-        "id": "Snudv-wKAR5j",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "Snudv-wKAR5j",
         "outputId": "bdb4b2af-872d-4ca0-f3af-ab84811c9f9c"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Collecting gliner\n",
             "  Downloading gliner-0.2.7-py3-none-any.whl (45 kB)\n",
@@ -130,22 +130,18 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "# download data\n",
-        "! wget https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json"
-      ],
+      "execution_count": 5,
       "metadata": {
-        "id": "ETFnglCGAcSp",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "ETFnglCGAcSp",
         "outputId": "aea51527-910e-4c20-83d6-07df2dbbe590"
       },
-      "execution_count": 5,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "--2024-07-16 10:45:25--  https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json\n",
             "Resolving huggingface.co (huggingface.co)... 18.164.174.118, 18.164.174.17, 18.164.174.23, ...\n",
@@ -165,6 +161,10 @@
             "\n"
           ]
         }
+      ],
+      "source": [
+        "# download data\n",
+        "! wget https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json"
       ]
     },
     {
@@ -181,44 +181,49 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "train_path = \"data.json\"\n",
-        "\n",
-        "with open(train_path, \"r\") as f:\n",
-        "    data = json.load(f)\n",
-        "\n",
-        "print('Dataset size:', len(data))\n",
-        "\n",
-        "random.shuffle(data)\n",
-        "print('Dataset is shuffled...')\n",
-        "\n",
-        "train_dataset = data[:int(len(data)*0.9)]\n",
-        "test_dataset = data[int(len(data)*0.9):]\n",
-        "\n",
-        "print('Dataset is splitted...')"
-      ],
+      "execution_count": 7,
       "metadata": {
-        "id": "aoQdh2QAGwFf",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "aoQdh2QAGwFf",
         "outputId": "b9fd130b-1d4d-4f53-ce23-8feb4bffbc7d"
       },
-      "execution_count": 7,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Dataset size: 19635\n",
             "Dataset is shuffled...\n",
             "Dataset is splitted...\n"
           ]
         }
+      ],
+      "source": [
+        "train_path = \"data.json\"\n",
+        "\n",
+        "with open(train_path, \"r\") as f:\n",
+        "    data = json.load(f)\n",
+        "\n",
+        "print('Dataset size:', len(data))\n",
+        "\n",
+        "random.shuffle(data)\n",
+        "print('Dataset is shuffled...')\n",
+        "\n",
+        "train_dataset = data[:int(len(data)*0.9)]\n",
+        "test_dataset = data[int(len(data)*0.9):]\n",
+        "\n",
+        "print('Dataset is splitted...')"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "mNBNsoUjG3vH"
+      },
+      "outputs": [],
       "source": [
         "import os\n",
         "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
@@ -229,18 +234,12 @@
         "from gliner.data_processing.collator import DataCollatorWithPadding, DataCollator\n",
         "from gliner.utils import load_config_as_namespace\n",
         "from gliner.data_processing import WordsSplitter, GLiNERDataset"
-      ],
-      "metadata": {
-        "id": "mNBNsoUjG3vH"
-      },
-      "execution_count": 8,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": 9,
       "metadata": {
-        "id": "b4X48dzJAR5m",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 472,
@@ -335,12 +334,13 @@
             "863316b633fd4fe88c79c7fb952b3eb7"
           ]
         },
+        "id": "b4X48dzJAR5m",
         "outputId": "f3316521-9906-4487-da0a-5b2df14e98a9"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
             "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
@@ -351,128 +351,128 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a5b28bae8ed64c349c379ea088177dcd",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "a5b28bae8ed64c349c379ea088177dcd"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "gliner_config.json:   0%|          | 0.00/477 [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4125956c26e14685998dde6c6d323cac",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4125956c26e14685998dde6c6d323cac"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "gliner_config.json:   0%|          | 0.00/477 [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "README.md:   0%|          | 0.00/4.84k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "00c2e7114c4e4dbcb35fbf2e42c90897",
               "version_major": 2,
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-              "model_id": "00c2e7114c4e4dbcb35fbf2e42c90897"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/4.84k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              ".gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"
-            ],
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-              "model_id": "5f554c8a330b4662bf54cd835869228c"
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+              "version_minor": 0
+            },
+            "text/plain": [
+              ".gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"
+            ]
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "pytorch_model.bin:   0%|          | 0.00/611M [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7ab3bf8c24db44e9955dcc77af36e4cd",
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "pytorch_model.bin:   0%|          | 0.00/611M [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "display_data",
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-              "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
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-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
             "  warnings.warn(\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "config.json:   0%|          | 0.00/578 [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8c4411a7e9ef4a6981c46f9bba67d566",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "8c4411a7e9ef4a6981c46f9bba67d566"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/578 [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8bbe3085ba76458583142659c02240dd",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "8bbe3085ba76458583142659c02240dd"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:560: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
             "  warnings.warn(\n"
@@ -509,8 +509,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "done\n"
           ]
@@ -531,6 +531,7 @@
       "outputs": [],
       "source": [
         "# calculate number of epochs\n",
+        "#Rajouter neg_span_masking:\n",
         "num_steps = 500\n",
         "batch_size = 8\n",
         "data_size = len(train_dataset)\n",
@@ -582,8 +583,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "config.json not found in /content/models/checkpoint-100\n",
             "WARNING:huggingface_hub.hub_mixin:config.json not found in /content/models/checkpoint-100\n",
@@ -609,15 +610,15 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Cristiano Ronaldo dos Santos Aveiro => Person\n",
             "Ballon d'Or => Award\n",
@@ -644,6 +645,11 @@
     }
   ],
   "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
@@ -661,17 +667,12 @@
       "version": "3.10.10"
     },
     "orig_nbformat": 4,
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4"
-    },
-    "accelerator": "GPU",
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
-        "a5b28bae8ed64c349c379ea088177dcd": {
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-          "model_name": "HBoxModel",
           "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
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             "_model_module": "@jupyter-widgets/controls",
@@ -683,17 +684,64 @@
             "_view_name": "HBoxView",
             "box_style": "",
             "children": [
-              "IPY_MODEL_fc5b3589df7a49349fe75079516e94b7",
-              "IPY_MODEL_5dd3b0770599485aa4569c25d6011daf",
-              "IPY_MODEL_12c1fe1e79644128870b20ddd6ac2b08"
+              "IPY_MODEL_b8b0c463a5ec4c5e83e3adbe424b5750",
+              "IPY_MODEL_8aececc2c9304726be79a6841d3e67b7",
+              "IPY_MODEL_ed44b59f24224bd2ac9608505cbe54b4"
             ],
-            "layout": "IPY_MODEL_aa722a17259542f1bc934fb578539260"
+            "layout": "IPY_MODEL_a01256737a80447aba5ffb0d6320177b"
           }
         },
-        "fc5b3589df7a49349fe75079516e94b7": {
+        "047e4456dd584e65ad72c256e67596a3": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "ProgressStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
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+        "0f58dd3a6e1f4e25910f8412a3f937d3": {
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-          "model_name": "HTMLModel",
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+          "model_name": "ProgressStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
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+        "11bd957d7a6a4f6dabb66dee38c29774": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
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+          "model_name": "HTMLModel",
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@@ -705,61 +753,55 @@
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             "description": "",
             "description_tooltip": null,
-            "layout": "IPY_MODEL_9b17b423c1dd40fa86ee865bc60fb81f",
+            "layout": "IPY_MODEL_cfa39bd49f2140aab6aa0139573b4762",
             "placeholder": "​",
-            "style": "IPY_MODEL_30d0d2fdf0874b6f93f9a1416af5cbc6",
-            "value": "Fetching 4 files: 100%"
+            "style": "IPY_MODEL_54dcc9bc81fc48b484cba6adf5000fcc",
+            "value": " 4/4 [00:05&lt;00:00,  1.32s/it]"
           }
         },
-        "5dd3b0770599485aa4569c25d6011daf": {
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             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "_model_name": "FloatProgressModel",
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             "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
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@@ -3202,10 +3191,25 @@
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@@ -3254,25 +3258,31 @@
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+            "placeholder": "​",
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@@ -3321,26 +3331,10 @@
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@@ -3389,19 +3383,26 @@
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diff --git a/gliner/config.py b/gliner/config.py
index c7af214d..a81d6832 100644
--- a/gliner/config.py
+++ b/gliner/config.py
@@ -22,6 +22,7 @@ def __init__(self,
                  max_types: int = 25,
                  max_len: int = 384,
                  words_splitter_type: str = "whitespace",
+                 neg_span_masking="global_w_threshold",
                  has_rnn: bool = True,
                  fuse_layers: bool = False,
                  embed_ent_token: bool = True,
@@ -68,6 +69,7 @@ def __init__(self,
         self.embed_ent_token = embed_ent_token
         self.ent_token = ent_token
         self.sep_token = sep_token
+        self.neg_span_masking=neg_span_masking
 
 # Register the configuration
 from transformers import CONFIG_MAPPING
diff --git a/gliner/modeling/base.py b/gliner/modeling/base.py
index 7013a550..712588f3 100644
--- a/gliner/modeling/base.py
+++ b/gliner/modeling/base.py
@@ -227,6 +227,7 @@ def forward(self,
                 span_idx: Optional[torch.LongTensor] = None,
                 span_mask: Optional[torch.LongTensor] = None,
                 labels: Optional[torch.FloatTensor] = None,
+                neg_span_masking: Optional[str] = None,
                 **kwargs
                 ):
 
@@ -243,7 +244,7 @@ def forward(self,
 
         loss = None
         if labels is not None:
-            loss = self.loss(scores, labels, prompts_embedding_mask, span_mask, **kwargs)
+            loss = self.loss(scores, labels, prompts_embedding_mask, span_mask, neg_span_masking, **kwargs)
 
         output = GLiNERModelOutput(
             logits=scores,
@@ -257,7 +258,7 @@ def forward(self,
     
     def loss(self, scores, labels, prompts_embedding_mask, mask_label,
                         alpha: float = -1., gamma: float = 0.0, label_smoothing: float = 0.0, 
-                        reduction: str = 'sum', **kwargs):
+                        reduction: str = 'sum', negative_rate: float = 0.75, neg_span_masking = None, **kwargs):
         
         batch_size = scores.shape[0]
         num_classes = prompts_embedding_mask.shape[-1]
@@ -274,6 +275,42 @@ def loss(self, scores, labels, prompts_embedding_mask, mask_label,
         
         all_losses = all_losses * mask_label.float()
 
+        if neg_span_masking is not None :
+
+            if neg_span_masking == "global_w_threshold":
+
+                # Adding negative examples 1a
+                #Permits to drop negative_rate percent of the negative examples
+                mask_negative_examples = (torch.rand_like(labels, dtype=torch.float) + labels > negative_rate).float()  
+                all_losses = all_losses * mask_negative_examples
+
+            elif neg_span_masking == "global_wo_threshold" :
+
+                #Adding negative examples 1b 
+                p = torch.sigmoid(scores) # We need to apply sigmoid so that all the scores will be between 0 and 1
+                random_mask = torch.bernoulli(1 - p) + labels 
+                mask_negative_examples = torch.where(labels == 1, torch.ones_like(labels), random_mask)
+                all_losses = all_losses*mask_negative_examples
+
+            elif neg_span_masking == "entity_w_threshold":
+                
+                # Adding negative examples 2a
+                #masking entities with no positive label
+                mask_negative_examples = labels.clone()
+                zero_rows = labels.sum(dim=1) == 0
+                mask_negative_examples[zero_rows] = (torch.rand((zero_rows.sum(), labels.size(1))) >= negative_rate).float()
+                all_losses = all_losses*mask_negative_examples
+
+            elif neg_span_masking == "entity_wo_threshold":
+                
+                print('To do')
+                
+            else:
+
+                warnings.warn(
+                    f"Invalid Value for config 'mask type': '{neg_span_masking}. ")
+
+
         if reduction == "mean":
             loss = all_losses.mean()
         elif reduction == 'sum':

From 05a061e18da61e3db09fb0841869e44e5f1cc1b4 Mon Sep 17 00:00:00 2001
From: Salim <adsalim007@gmail.com>
Date: Fri, 28 Feb 2025 11:15:19 +0100
Subject: [PATCH 3/3] v1 span masking

---
 .gitignore              |    1 +
 examples/finetune.ipynb | 1783 +++++++++++++++++++--------------------
 gliner/config.py        |    2 +-
 gliner/modeling/base.py |   23 +-
 4 files changed, 903 insertions(+), 906 deletions(-)

diff --git a/.gitignore b/.gitignore
index 42d6f0ed..9cae7ea8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,7 @@ __pycache__/
 
 #data
 data.json
+data.json*
 
 #logs
 logs/
diff --git a/examples/finetune.ipynb b/examples/finetune.ipynb
index 9d5b70ed..f6e4fbd1 100644
--- a/examples/finetune.ipynb
+++ b/examples/finetune.ipynb
@@ -4,16 +4,16 @@
       "cell_type": "code",
       "execution_count": 1,
       "metadata": {
+        "id": "Snudv-wKAR5j",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "Snudv-wKAR5j",
         "outputId": "bdb4b2af-872d-4ca0-f3af-ab84811c9f9c"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Collecting gliner\n",
             "  Downloading gliner-0.2.7-py3-none-any.whl (45 kB)\n",
@@ -130,18 +130,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "source": [
+        "# download data\n",
+        "! wget https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json"
+      ],
       "metadata": {
+        "id": "ETFnglCGAcSp",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "ETFnglCGAcSp",
         "outputId": "aea51527-910e-4c20-83d6-07df2dbbe590"
       },
+      "execution_count": 5,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "--2024-07-16 10:45:25--  https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json\n",
             "Resolving huggingface.co (huggingface.co)... 18.164.174.118, 18.164.174.17, 18.164.174.23, ...\n",
@@ -161,10 +165,6 @@
             "\n"
           ]
         }
-      ],
-      "source": [
-        "# download data\n",
-        "! wget https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json"
       ]
     },
     {
@@ -181,25 +181,6 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "aoQdh2QAGwFf",
-        "outputId": "b9fd130b-1d4d-4f53-ce23-8feb4bffbc7d"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Dataset size: 19635\n",
-            "Dataset is shuffled...\n",
-            "Dataset is splitted...\n"
-          ]
-        }
-      ],
       "source": [
         "train_path = \"data.json\"\n",
         "\n",
@@ -215,15 +196,29 @@
         "test_dataset = data[int(len(data)*0.9):]\n",
         "\n",
         "print('Dataset is splitted...')"
+      ],
+      "metadata": {
+        "id": "aoQdh2QAGwFf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b9fd130b-1d4d-4f53-ce23-8feb4bffbc7d"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset size: 19635\n",
+            "Dataset is shuffled...\n",
+            "Dataset is splitted...\n"
+          ]
+        }
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "mNBNsoUjG3vH"
-      },
-      "outputs": [],
       "source": [
         "import os\n",
         "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
@@ -234,12 +229,18 @@
         "from gliner.data_processing.collator import DataCollatorWithPadding, DataCollator\n",
         "from gliner.utils import load_config_as_namespace\n",
         "from gliner.data_processing import WordsSplitter, GLiNERDataset"
-      ]
+      ],
+      "metadata": {
+        "id": "mNBNsoUjG3vH"
+      },
+      "execution_count": 8,
+      "outputs": []
     },
     {
       "cell_type": "code",
       "execution_count": 9,
       "metadata": {
+        "id": "b4X48dzJAR5m",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 472,
@@ -334,13 +335,12 @@
             "863316b633fd4fe88c79c7fb952b3eb7"
           ]
         },
-        "id": "b4X48dzJAR5m",
         "outputId": "f3316521-9906-4487-da0a-5b2df14e98a9"
       },
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stderr",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
             "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
@@ -351,128 +351,128 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "a5b28bae8ed64c349c379ea088177dcd",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a5b28bae8ed64c349c379ea088177dcd"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "4125956c26e14685998dde6c6d323cac",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               "gliner_config.json:   0%|          | 0.00/477 [00:00<?, ?B/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4125956c26e14685998dde6c6d323cac"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "00c2e7114c4e4dbcb35fbf2e42c90897",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               "README.md:   0%|          | 0.00/4.84k [00:00<?, ?B/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "00c2e7114c4e4dbcb35fbf2e42c90897"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "5f554c8a330b4662bf54cd835869228c",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               ".gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5f554c8a330b4662bf54cd835869228c"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "7ab3bf8c24db44e9955dcc77af36e4cd",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               "pytorch_model.bin:   0%|          | 0.00/611M [00:00<?, ?B/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7ab3bf8c24db44e9955dcc77af36e4cd"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ff81e2a4b77d410f86e2734f307af0f8",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ff81e2a4b77d410f86e2734f307af0f8"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stderr",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
             "  warnings.warn(\n"
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "8c4411a7e9ef4a6981c46f9bba67d566",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               "config.json:   0%|          | 0.00/578 [00:00<?, ?B/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8c4411a7e9ef4a6981c46f9bba67d566"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "8bbe3085ba76458583142659c02240dd",
-              "version_major": 2,
-              "version_minor": 0
-            },
             "text/plain": [
               "spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
-            ]
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8bbe3085ba76458583142659c02240dd"
+            }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stderr",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:560: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
             "  warnings.warn(\n"
@@ -509,8 +509,8 @@
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "done\n"
           ]
@@ -531,7 +531,6 @@
       "outputs": [],
       "source": [
         "# calculate number of epochs\n",
-        "#Rajouter neg_span_masking:\n",
         "num_steps = 500\n",
         "batch_size = 8\n",
         "data_size = len(train_dataset)\n",
@@ -583,8 +582,8 @@
       },
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stderr",
           "text": [
             "config.json not found in /content/models/checkpoint-100\n",
             "WARNING:huggingface_hub.hub_mixin:config.json not found in /content/models/checkpoint-100\n",
@@ -610,15 +609,15 @@
       },
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stderr",
           "text": [
             "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
           ]
         },
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Cristiano Ronaldo dos Santos Aveiro => Person\n",
             "Ballon d'Or => Award\n",
@@ -645,11 +644,6 @@
     }
   ],
   "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
@@ -667,12 +661,17 @@
       "version": "3.10.10"
     },
     "orig_nbformat": 4,
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU",
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
-        "00c2e7114c4e4dbcb35fbf2e42c90897": {
+        "a5b28bae8ed64c349c379ea088177dcd": {
           "model_module": "@jupyter-widgets/controls",
-          "model_module_version": "1.5.0",
           "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
           "state": {
             "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
@@ -684,64 +683,17 @@
             "_view_name": "HBoxView",
             "box_style": "",
             "children": [
-              "IPY_MODEL_b8b0c463a5ec4c5e83e3adbe424b5750",
-              "IPY_MODEL_8aececc2c9304726be79a6841d3e67b7",
-              "IPY_MODEL_ed44b59f24224bd2ac9608505cbe54b4"
+              "IPY_MODEL_fc5b3589df7a49349fe75079516e94b7",
+              "IPY_MODEL_5dd3b0770599485aa4569c25d6011daf",
+              "IPY_MODEL_12c1fe1e79644128870b20ddd6ac2b08"
             ],
-            "layout": "IPY_MODEL_a01256737a80447aba5ffb0d6320177b"
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-            "_model_module_version": "1.5.0",
-            "_model_name": "ProgressStyleModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
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-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
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-            "_model_module_version": "1.5.0",
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-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "description_width": ""
+            "layout": "IPY_MODEL_aa722a17259542f1bc934fb578539260"
           }
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-        "12c1fe1e79644128870b20ddd6ac2b08": {
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+          "model_module_version": "1.5.0",
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@@ -753,55 +705,61 @@
             "_view_name": "HTMLView",
             "description": "",
             "description_tooltip": null,
-            "layout": "IPY_MODEL_cfa39bd49f2140aab6aa0139573b4762",
+            "layout": "IPY_MODEL_9b17b423c1dd40fa86ee865bc60fb81f",
             "placeholder": "​",
-            "style": "IPY_MODEL_54dcc9bc81fc48b484cba6adf5000fcc",
-            "value": " 4/4 [00:05&lt;00:00,  1.32s/it]"
+            "style": "IPY_MODEL_30d0d2fdf0874b6f93f9a1416af5cbc6",
+            "value": "Fetching 4 files: 100%"
           }
         },
-        "184c482bf66247adae3b799e20f23b5f": {
+        "5dd3b0770599485aa4569c25d6011daf": {
           "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
           "model_module_version": "1.5.0",
-          "model_name": "DescriptionStyleModel",
           "state": {
+            "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "_model_name": "DescriptionStyleModel",
+            "_model_name": "FloatProgressModel",
             "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "description_width": ""
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
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+            "description_tooltip": null,
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+            "placeholder": "​",
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-        "cfa39bd49f2140aab6aa0139573b4762": {
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           "model_module": "@jupyter-widgets/base",
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@@ -2926,10 +2860,10 @@
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@@ -2978,10 +2912,25 @@
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+            "_view_module_version": "1.2.0",
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@@ -3030,31 +2979,26 @@
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-            "placeholder": "​",
-            "style": "IPY_MODEL_cede5b52edd443d586a364b2ac504d13",
-            "value": "gliner_config.json: 100%"
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
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@@ -3103,10 +3047,47 @@
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           "model_module_version": "1.5.0",
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+            "_model_module_version": "1.5.0",
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+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
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+            "children": [
+              "IPY_MODEL_be0812f8a8fe4d27b0d231baa15a2540",
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@@ -3118,31 +3099,61 @@
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-            "layout": "IPY_MODEL_6c596d654f5644e0a542aa3ec50c3a80",
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-            "style": "IPY_MODEL_184c482bf66247adae3b799e20f23b5f",
-            "value": " 4.84k/4.84k [00:00&lt;00:00, 49.1kB/s]"
+            "style": "IPY_MODEL_11bd957d7a6a4f6dabb66dee38c29774",
+            "value": "spm.model: 100%"
           }
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+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_ea4b1eb5beb44efca91af35b3ae49be7",
+            "placeholder": "​",
+            "style": "IPY_MODEL_863316b633fd4fe88c79c7fb952b3eb7",
+            "value": " 2.46M/2.46M [00:00&lt;00:00, 11.4MB/s]"
+          }
+        },
+        "8c3d86c1d7ac474bb169a653eed0b6a7": {
           "model_module": "@jupyter-widgets/base",
-          "model_module_version": "1.2.0",
           "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -3191,25 +3202,10 @@
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-        "faa363e58a504b88a8288eb6e1af0fdb": {
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-          "model_module_version": "1.5.0",
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-            "_model_module_version": "1.5.0",
-            "_model_name": "DescriptionStyleModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "description_width": ""
-          }
-        },
-        "fb6b6f6ce9f547d5abe169eb84aabc61": {
+        "cac19ae4fea247fcad328ca2a3d232b2": {
           "model_module": "@jupyter-widgets/base",
-          "model_module_version": "1.2.0",
           "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -3258,31 +3254,25 @@
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-        "fc5b3589df7a49349fe75079516e94b7": {
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           "model_module_version": "1.5.0",
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-            "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "_model_name": "HTMLModel",
+            "_model_name": "DescriptionStyleModel",
             "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HTMLView",
-            "description": "",
-            "description_tooltip": null,
-            "layout": "IPY_MODEL_9b17b423c1dd40fa86ee865bc60fb81f",
-            "placeholder": "​",
-            "style": "IPY_MODEL_30d0d2fdf0874b6f93f9a1416af5cbc6",
-            "value": "Fetching 4 files: 100%"
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
           }
         },
-        "fcf1a8c3e2244ac5843481043cf3df1f": {
+        "b0c4368959f245f9b29f6c7b7129f5c6": {
           "model_module": "@jupyter-widgets/base",
-          "model_module_version": "1.2.0",
           "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -3331,10 +3321,26 @@
             "width": null
           }
         },
-        "fea72cbbd2624cbcb325c379fd0f72c8": {
+        "ad63a6a69f82432bb02ea18702abbf65": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "ea4b1eb5beb44efca91af35b3ae49be7": {
           "model_module": "@jupyter-widgets/base",
-          "model_module_version": "1.2.0",
           "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -3383,26 +3389,19 @@
             "width": null
           }
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-        "ff81e2a4b77d410f86e2734f307af0f8": {
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           "model_module_version": "1.5.0",
-          "model_name": "HBoxModel",
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-            "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "_model_name": "HBoxModel",
+            "_model_name": "DescriptionStyleModel",
             "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HBoxView",
-            "box_style": "",
-            "children": [
-              "IPY_MODEL_91a3abca5e8d4866906d30959e2cb19b",
-              "IPY_MODEL_79db287bbe9e431093416d4abf102f30",
-              "IPY_MODEL_7a46e42c5a9f44918ff5698a158a2ede"
-            ],
-            "layout": "IPY_MODEL_292ad99ffdad492ea8b30a98a0994af1"
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
           }
         }
       }
diff --git a/gliner/config.py b/gliner/config.py
index a81d6832..a5fe60ec 100644
--- a/gliner/config.py
+++ b/gliner/config.py
@@ -22,7 +22,7 @@ def __init__(self,
                  max_types: int = 25,
                  max_len: int = 384,
                  words_splitter_type: str = "whitespace",
-                 neg_span_masking="global_w_threshold",
+                 neg_span_masking: str = None,
                  has_rnn: bool = True,
                  fuse_layers: bool = False,
                  embed_ent_token: bool = True,
diff --git a/gliner/modeling/base.py b/gliner/modeling/base.py
index 712588f3..ebfa6a4d 100644
--- a/gliner/modeling/base.py
+++ b/gliner/modeling/base.py
@@ -227,7 +227,6 @@ def forward(self,
                 span_idx: Optional[torch.LongTensor] = None,
                 span_mask: Optional[torch.LongTensor] = None,
                 labels: Optional[torch.FloatTensor] = None,
-                neg_span_masking: Optional[str] = None,
                 **kwargs
                 ):
 
@@ -244,7 +243,7 @@ def forward(self,
 
         loss = None
         if labels is not None:
-            loss = self.loss(scores, labels, prompts_embedding_mask, span_mask, neg_span_masking, **kwargs)
+            loss = self.loss(scores, labels, prompts_embedding_mask, span_mask, **kwargs)
 
         output = GLiNERModelOutput(
             logits=scores,
@@ -258,7 +257,7 @@ def forward(self,
     
     def loss(self, scores, labels, prompts_embedding_mask, mask_label,
                         alpha: float = -1., gamma: float = 0.0, label_smoothing: float = 0.0, 
-                        reduction: str = 'sum', negative_rate: float = 0.75, neg_span_masking = None, **kwargs):
+                        reduction: str = 'sum', negative_rate: float = 0.75, neg_span_masking: str = None, **kwargs):
         
         batch_size = scores.shape[0]
         num_classes = prompts_embedding_mask.shape[-1]
@@ -267,7 +266,7 @@ def loss(self, scores, labels, prompts_embedding_mask, mask_label,
         labels = labels.view(-1, num_classes)
         
         all_losses = self._loss(scores, labels, alpha, gamma, label_smoothing)
-        #print
+
         masked_loss = all_losses.view(batch_size, -1, num_classes) * prompts_embedding_mask.unsqueeze(1)
         all_losses = masked_loss.view(-1, num_classes)
 
@@ -279,36 +278,34 @@ def loss(self, scores, labels, prompts_embedding_mask, mask_label,
 
             if neg_span_masking == "global_w_threshold":
 
-                # Adding negative examples 1a
-                #Permits to drop negative_rate percent of the negative examples
                 mask_negative_examples = (torch.rand_like(labels, dtype=torch.float) + labels > negative_rate).float()  
                 all_losses = all_losses * mask_negative_examples
 
             elif neg_span_masking == "global_wo_threshold" :
 
-                #Adding negative examples 1b 
-                p = torch.sigmoid(scores) # We need to apply sigmoid so that all the scores will be between 0 and 1
+                p = torch.sigmoid(scores) 
                 random_mask = torch.bernoulli(1 - p) + labels 
                 mask_negative_examples = torch.where(labels == 1, torch.ones_like(labels), random_mask)
                 all_losses = all_losses*mask_negative_examples
 
             elif neg_span_masking == "entity_w_threshold":
                 
-                # Adding negative examples 2a
-                #masking entities with no positive label
                 mask_negative_examples = labels.clone()
                 zero_rows = labels.sum(dim=1) == 0
                 mask_negative_examples[zero_rows] = (torch.rand((zero_rows.sum(), labels.size(1))) >= negative_rate).float()
                 all_losses = all_losses*mask_negative_examples
 
             elif neg_span_masking == "entity_wo_threshold":
-                
-                print('To do')
+
+                p = torch.sigmoid(scores)
+                mask = labels.clone()
+                rows_to_sample = labels.sum(dim=1) == 0
+                mask[rows_to_sample] = torch.bernoulli(p[rows_to_sample])
                 
             else:
 
                 warnings.warn(
-                    f"Invalid Value for config 'mask type': '{neg_span_masking}. ")
+                    f"Invalid Value for config 'neg_span_masking': '{neg_span_masking}. ")
 
 
         if reduction == "mean":