---
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-XML-RoBERTa-BIOBERT
  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: 0.937247539398077
    - name: Recall
      type: recall
      value: 0.964689348246179
    - name: F1
      type: f1
      value: 0.9507704738269752
    - name: Accuracy
      type: accuracy
      value: 0.9773235561218265
---

<!-- 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-XML-RoBERTa-BIOBERT

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: 0.1065
- Precision: 0.9372
- Recall: 0.9647
- F1: 0.9508
- Accuracy: 0.9773

## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1305        | 1.0   | 2447  | 0.1005          | 0.9298    | 0.9680 | 0.9485 | 0.9747   |
| 0.0874        | 2.0   | 4894  | 0.0981          | 0.9406    | 0.9711 | 0.9556 | 0.9781   |
| 0.0782        | 3.0   | 7341  | 0.1023          | 0.9245    | 0.9577 | 0.9408 | 0.9747   |
| 0.0807        | 4.0   | 9788  | 0.1042          | 0.9316    | 0.9567 | 0.9440 | 0.9753   |
| 0.0437        | 5.0   | 12235 | 0.1065          | 0.9372    | 0.9647 | 0.9508 | 0.9773   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1