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---
library_name: transformers
base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-conll03-english-finetuned-ner
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: 0.947671764437343
- name: Recall
type: recall
value: 0.9724776014522457
- name: F1
type: f1
value: 0.9599144533394989
- name: Accuracy
type: accuracy
value: 0.9809696788972173
---
<!-- 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. -->
# xlm-roberta-large-finetuned-conll03-english-finetuned-ner
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0876
- Precision: 0.9477
- Recall: 0.9725
- F1: 0.9599
- Accuracy: 0.9810
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.255 | 1.0 | 612 | 0.0956 | 0.9305 | 0.9638 | 0.9468 | 0.9749 |
| 0.0997 | 2.0 | 1224 | 0.0871 | 0.9397 | 0.9740 | 0.9565 | 0.9795 |
| 0.0711 | 3.0 | 1836 | 0.0848 | 0.9474 | 0.9718 | 0.9595 | 0.9806 |
| 0.0552 | 4.0 | 2448 | 0.0860 | 0.9464 | 0.9744 | 0.9602 | 0.9808 |
| 0.0354 | 5.0 | 3060 | 0.0876 | 0.9477 | 0.9725 | 0.9599 | 0.9810 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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