End of training

README.md

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+---
+library_name: transformers
+base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
+tags:
+- generated_from_trainer
+datasets:
+- biobert_json
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-large-finetuned-conll03-english-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: biobert_json
+      type: biobert_json
+      config: Biobert_json
+      split: validation
+      args: Biobert_json
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.947671764437343
+    - name: Recall
+      type: recall
+      value: 0.9724776014522457
+    - name: F1
+      type: f1
+      value: 0.9599144533394989
+    - name: Accuracy
+      type: accuracy
+      value: 0.9809696788972173
+---
+
+<!-- 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-roberta-large-finetuned-conll03-english-finetuned-ner
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) on the biobert_json dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0876
+- Precision: 0.9477
+- Recall: 0.9725
+- F1: 0.9599
+- Accuracy: 0.9810
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.255         | 1.0   | 612  | 0.0956          | 0.9305    | 0.9638 | 0.9468 | 0.9749   |
+| 0.0997        | 2.0   | 1224 | 0.0871          | 0.9397    | 0.9740 | 0.9565 | 0.9795   |
+| 0.0711        | 3.0   | 1836 | 0.0848          | 0.9474    | 0.9718 | 0.9595 | 0.9806   |
+| 0.0552        | 4.0   | 2448 | 0.0860          | 0.9464    | 0.9744 | 0.9602 | 0.9808   |
+| 0.0354        | 5.0   | 3060 | 0.0876          | 0.9477    | 0.9725 | 0.9599 | 0.9810   |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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