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-t4
+  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-t4
+
+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: 0.1139
+- Precision: 0.9234
+- Recall: 0.9548
+- F1: 0.9388
+- Accuracy: 0.9786
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 4
+- 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1235        | 1.0   | 2447  | 0.0949          | 0.9076    | 0.9524 | 0.9294 | 0.9738   |
+| 0.0859        | 2.0   | 4894  | 0.1034          | 0.9222    | 0.9597 | 0.9406 | 0.9778   |
+| 0.063         | 3.0   | 7341  | 0.1005          | 0.9330    | 0.9600 | 0.9463 | 0.9807   |
+| 0.059         | 4.0   | 9788  | 0.1065          | 0.9350    | 0.9577 | 0.9463 | 0.9806   |
+| 0.0513        | 5.0   | 12235 | 0.1139          | 0.9234    | 0.9548 | 0.9388 | 0.9786   |
+
+
+### 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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