---
library_name: transformers
base_model: raulgdp/xml-roberta-large-finetuned-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xml-roberta-large-ner-finetuned-biomedical-conT4-16
  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: 0.0755
- Precision: 0.9291
- Recall: 0.9569
- F1: 0.9428
- Accuracy: 0.9798

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 306  | 0.1016          | 0.9102    | 0.9308 | 0.9204 | 0.9711   |
| 0.6706        | 2.0   | 612  | 0.0809          | 0.9237    | 0.9598 | 0.9414 | 0.9784   |
| 0.6706        | 3.0   | 918  | 0.0696          | 0.9371    | 0.9612 | 0.9490 | 0.9817   |
| 0.079         | 4.0   | 1224 | 0.0738          | 0.9318    | 0.9582 | 0.9448 | 0.9803   |
| 0.0564        | 5.0   | 1530 | 0.0755          | 0.9291    | 0.9569 | 0.9428 | 0.9798   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3