End of training

README.md

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+---
+library_name: transformers
+base_model: raulgdp/xml-roberta-large-finetuned-ner
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: la-xml-roberta-large-ner-finetuned-biomedical
+  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. -->
+
+# la-xml-roberta-large-ner-finetuned-biomedical
+
+This model is a fine-tuned version of [raulgdp/xml-roberta-large-finetuned-ner](https://huggingface.co/raulgdp/xml-roberta-large-finetuned-ner) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0856
+- Precision: 0.9255
+- Recall: 0.9564
+- F1: 0.9407
+- Accuracy: 0.9788
+
+## 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: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- 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
+- lr_scheduler_warmup_steps: 200
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.6562        | 1.0   | 612  | 0.0902          | 0.9225    | 0.9397 | 0.9310 | 0.9740   |
+| 0.1069        | 2.0   | 1224 | 0.0833          | 0.9143    | 0.9550 | 0.9342 | 0.9771   |
+| 0.0788        | 3.0   | 1836 | 0.0873          | 0.9242    | 0.9576 | 0.9406 | 0.9785   |
+| 0.0619        | 4.0   | 2448 | 0.0863          | 0.9282    | 0.9557 | 0.9417 | 0.9790   |
+| 0.0466        | 5.0   | 3060 | 0.0856          | 0.9255    | 0.9564 | 0.9407 | 0.9788   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

model.safetensors

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