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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-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
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