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---
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: 0.9497881598534296
- name: Recall
type: recall
value: 0.9714235521461615
- name: F1
type: f1
value: 0.9604840343919173
- name: Accuracy
type: accuracy
value: 0.981362755330252
---
<!-- 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: 0.0946
- Precision: 0.9498
- Recall: 0.9714
- F1: 0.9605
- Accuracy: 0.9814
## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1306 | 1.0 | 1224 | 0.1013 | 0.9299 | 0.9609 | 0.9451 | 0.9735 |
| 0.0996 | 2.0 | 2448 | 0.0932 | 0.9383 | 0.9656 | 0.9517 | 0.9777 |
| 0.0608 | 3.0 | 3672 | 0.0865 | 0.9493 | 0.9720 | 0.9605 | 0.9813 |
| 0.0445 | 4.0 | 4896 | 0.0927 | 0.9531 | 0.9729 | 0.9629 | 0.9819 |
| 0.0327 | 5.0 | 6120 | 0.0946 | 0.9498 | 0.9714 | 0.9605 | 0.9814 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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