---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-TCR-NER_data-univner_half
  results: []
---

<!-- 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. -->

# scenario-TCR-NER_data-univner_half

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1160
- Precision: 0.8555
- Recall: 0.8189
- F1: 0.8368
- Accuracy: 0.9828

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1189        | 0.58  | 500  | 0.0623          | 0.8010    | 0.8531 | 0.8262 | 0.9822   |
| 0.0469        | 1.17  | 1000 | 0.0640          | 0.8246    | 0.8567 | 0.8404 | 0.9833   |
| 0.0348        | 1.75  | 1500 | 0.0668          | 0.8335    | 0.8550 | 0.8441 | 0.9834   |
| 0.0242        | 2.33  | 2000 | 0.0734          | 0.8202    | 0.8538 | 0.8367 | 0.9826   |
| 0.0215        | 2.91  | 2500 | 0.0717          | 0.8455    | 0.8598 | 0.8526 | 0.9843   |
| 0.0142        | 3.5   | 3000 | 0.0802          | 0.8383    | 0.8424 | 0.8404 | 0.9836   |
| 0.0144        | 4.08  | 3500 | 0.0836          | 0.8443    | 0.8554 | 0.8499 | 0.9843   |
| 0.0103        | 4.66  | 4000 | 0.0811          | 0.8479    | 0.8590 | 0.8534 | 0.9844   |
| 0.0087        | 5.24  | 4500 | 0.0887          | 0.8364    | 0.8628 | 0.8494 | 0.9840   |
| 0.0092        | 5.83  | 5000 | 0.0876          | 0.8367    | 0.8430 | 0.8399 | 0.9833   |
| 0.0076        | 6.41  | 5500 | 0.1004          | 0.8440    | 0.8495 | 0.8468 | 0.9841   |
| 0.007         | 6.99  | 6000 | 0.1080          | 0.8215    | 0.8518 | 0.8364 | 0.9830   |
| 0.0055        | 7.58  | 6500 | 0.0988          | 0.8454    | 0.8358 | 0.8406 | 0.9831   |
| 0.0055        | 8.16  | 7000 | 0.0950          | 0.8485    | 0.8461 | 0.8473 | 0.9839   |
| 0.0044        | 8.74  | 7500 | 0.1001          | 0.8456    | 0.8414 | 0.8435 | 0.9836   |
| 0.004         | 9.32  | 8000 | 0.1084          | 0.8340    | 0.8495 | 0.8417 | 0.9834   |
| 0.004         | 9.91  | 8500 | 0.1175          | 0.8351    | 0.8505 | 0.8427 | 0.9829   |
| 0.0033        | 10.49 | 9000 | 0.1160          | 0.8555    | 0.8189 | 0.8368 | 0.9828   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3