---
library_name: transformers
base_model: raulgdp/xml-roberta-large-finetuned-ner
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-XMLR-CM-V1
  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: 0.9336523819882532
    - name: Recall
      type: recall
      value: 0.9595349877040018
    - name: F1
      type: f1
      value: 0.9464167585446528
    - name: Accuracy
      type: accuracy
      value: 0.9819591471596839
---

<!-- 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. -->

# NER-finetuning-XMLR-CM-V1

This model is a fine-tuned version of [raulgdp/xml-roberta-large-finetuned-ner](https://huggingface.co/raulgdp/xml-roberta-large-finetuned-ner) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0849
- Precision: 0.9337
- Recall: 0.9595
- F1: 0.9464
- Accuracy: 0.9820

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2697        | 1.0   | 612  | 0.0995          | 0.9022    | 0.9392 | 0.9203 | 0.9726   |
| 0.0954        | 2.0   | 1224 | 0.0909          | 0.9171    | 0.9586 | 0.9374 | 0.9778   |
| 0.0661        | 3.0   | 1836 | 0.0789          | 0.9337    | 0.9581 | 0.9457 | 0.9816   |
| 0.0533        | 4.0   | 2448 | 0.0853          | 0.9317    | 0.9594 | 0.9454 | 0.9811   |
| 0.035         | 5.0   | 3060 | 0.0849          | 0.9337    | 0.9595 | 0.9464 | 0.9820   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1