Model save

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: xlm-large-finetuned-ner-covidmed-v5
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# xlm-large-finetuned-ner-covidmed-v5
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0778
+- Accuracy: 0.9818
+- Precision: 0.9105
+- Recall: 0.9395
+- F1: 0.9241
+- Age Precision: 0.9692
+- Age Recall: 0.9725
+- Age F1-score: 0.9708
+- Date Precision: 0.9832
+- Date Recall: 0.9927
+- Date F1-score: 0.9880
+- Gender Precision: 0.9539
+- Gender Recall: 0.9848
+- Gender F1-score: 0.9691
+- Job Precision: 0.6667
+- Job Recall: 0.8208
+- Job F1-score: 0.7358
+- Location Precision: 0.9394
+- Location Recall: 0.9532
+- Location F1-score: 0.9462
+- Name Precision: 0.9128
+- Name Recall: 0.9214
+- Name F1-score: 0.9171
+- Organization Precision: 0.8692
+- Organization Recall: 0.8962
+- Organization F1-score: 0.8825
+- Patient Id Precision: 0.9786
+- Patient Id Recall: 0.9796
+- Patient Id F1-score: 0.9791
+- Symptom And Disease Precision: 0.8632
+- Symptom And Disease Recall: 0.8944
+- Symptom And Disease F1-score: 0.8785
+- Transportation Precision: 0.9692
+- Transportation Recall: 0.9793
+- Transportation F1-score: 0.9742
+- Micro avg Precision: 0.9369
+- Micro avg Recall: 0.9536
+- Micro avg F1-score: 0.9452
+- Macro avg Precision: 0.9105
+- Macro avg Recall: 0.9395
+- Macro avg F1-score: 0.9241
+- Weighted avg Precision: 0.9381
+- Weighted avg Recall: 0.9536
+- Weighted avg F1-score: 0.9457
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Age Precision | Age Recall | Age F1-score | Date Precision | Date Recall | Date F1-score | Gender Precision | Gender Recall | Gender F1-score | Job Precision | Job Recall | Job F1-score | Location Precision | Location Recall | Location F1-score | Name Precision | Name Recall | Name F1-score | Organization Precision | Organization Recall | Organization F1-score | Patient Id Precision | Patient Id Recall | Patient Id F1-score | Symptom And Disease Precision | Symptom And Disease Recall | Symptom And Disease F1-score | Transportation Precision | Transportation Recall | Transportation F1-score | Micro avg Precision | Micro avg Recall | Micro avg F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------------:|:----------:|:------------:|:--------------:|:-----------:|:-------------:|:----------------:|:-------------:|:---------------:|:-------------:|:----------:|:------------:|:------------------:|:---------------:|:-----------------:|:--------------:|:-----------:|:-------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:-----------------:|:-------------------:|:-----------------------------:|:--------------------------:|:----------------------------:|:------------------------:|:---------------------:|:-----------------------:|:-------------------:|:----------------:|:------------------:|:-------------------:|:----------------:|:------------------:|:----------------------:|:-------------------:|:---------------------:|
+| No log        | 1.0   | 158  | 0.0963          | 0.9733   | 0.7903    | 0.9007 | 0.8387 | 0.8174        | 0.9845     | 0.8932       | 0.9814         | 0.9915      | 0.9865        | 0.7897           | 0.9913        | 0.8791          | 0.4495        | 0.7457     | 0.5609       | 0.9119             | 0.9302          | 0.9210            | 0.8216         | 0.8836      | 0.8515        | 0.7558                 | 0.8911              | 0.8179                | 0.9286               | 0.9791            | 0.9531              | 0.7831                        | 0.8741                     | 0.8261                       | 0.6636                   | 0.7358                | 0.6978                  | 0.8717              | 0.9371           | 0.9032             | 0.7903              | 0.9007           | 0.8387             | 0.8790                 | 0.9371              | 0.9059                |
+| No log        | 2.0   | 316  | 0.0762          | 0.9797   | 0.8970    | 0.9193 | 0.9078 | 0.9659        | 0.9725     | 0.9692       | 0.9791         | 0.9897      | 0.9844        | 0.9479           | 0.9848        | 0.9660          | 0.6392        | 0.7168     | 0.6757       | 0.9368             | 0.9514          | 0.9440            | 0.8627         | 0.9088      | 0.8851        | 0.8793                 | 0.8885              | 0.8839                | 0.9752               | 0.9791            | 0.9771              | 0.8352                        | 0.8477                     | 0.8414                       | 0.9485                   | 0.9534                | 0.9509                  | 0.9308              | 0.9451           | 0.9379             | 0.8970              | 0.9193           | 0.9078             | 0.9314                 | 0.9451              | 0.9382                |
+| No log        | 3.0   | 474  | 0.0761          | 0.9812   | 0.9018    | 0.9405 | 0.9199 | 0.9468        | 0.9794     | 0.9628       | 0.9844         | 0.9933      | 0.9889        | 0.9459           | 0.9848        | 0.9650          | 0.6606        | 0.8439     | 0.7411       | 0.9222             | 0.9525          | 0.9371            | 0.8981         | 0.9151      | 0.9065        | 0.8672                 | 0.8807              | 0.8739                | 0.9729               | 0.9850            | 0.9789              | 0.8555                        | 0.8908                     | 0.8728                       | 0.9643                   | 0.9793                | 0.9717                  | 0.9266              | 0.9536           | 0.9399             | 0.9018              | 0.9405           | 0.9199             | 0.9279                 | 0.9536              | 0.9404                |
+| 0.1535        | 4.0   | 632  | 0.0778          | 0.9818   | 0.9105    | 0.9395 | 0.9241 | 0.9692        | 0.9725     | 0.9708       | 0.9832         | 0.9927      | 0.9880        | 0.9539           | 0.9848        | 0.9691          | 0.6667        | 0.8208     | 0.7358       | 0.9394             | 0.9532          | 0.9462            | 0.9128         | 0.9214      | 0.9171        | 0.8692                 | 0.8962              | 0.8825                | 0.9786               | 0.9796            | 0.9791              | 0.8632                        | 0.8944                     | 0.8785                       | 0.9692                   | 0.9793                | 0.9742                  | 0.9369              | 0.9536           | 0.9452             | 0.9105              | 0.9395           | 0.9241             | 0.9381                 | 0.9536              | 0.9457                |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.6.0
+- Tokenizers 0.21.1

config.json

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+    "XLMRobertaForTokenClassification"
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+  "id2label": {
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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special_tokens_map.json

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tokenizer_config.json

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+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}

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