---
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. -->

# 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: 0.1139
- 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