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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: 0.937247539398077
- 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
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