---
base_model: FacebookAI/xlm-roberta-large
library_name: peft
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large-finetuned-ner
  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. -->

# xlm-roberta-large-finetuned-ner

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1297
- Precision: 0.9233
- Recall: 0.9608
- F1: 0.9416
- Accuracy: 0.9764

## 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: 0.0002
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2578        | 1.0   | 1167  | 0.1724          | 0.8145    | 0.8758 | 0.8440 | 0.9436   |
| 0.1792        | 2.0   | 2334  | 0.1305          | 0.8636    | 0.9089 | 0.8857 | 0.9603   |
| 0.1224        | 3.0   | 3501  | 0.1390          | 0.8714    | 0.9398 | 0.9043 | 0.9617   |
| 0.0894        | 4.0   | 4668  | 0.1201          | 0.8856    | 0.9444 | 0.9140 | 0.9646   |
| 0.0731        | 5.0   | 5835  | 0.1264          | 0.8848    | 0.9497 | 0.9161 | 0.9696   |
| 0.0597        | 6.0   | 7002  | 0.1247          | 0.9231    | 0.9516 | 0.9371 | 0.9751   |
| 0.044         | 7.0   | 8169  | 0.1141          | 0.9177    | 0.9468 | 0.9320 | 0.9749   |
| 0.0373        | 8.0   | 9336  | 0.1235          | 0.9126    | 0.9597 | 0.9355 | 0.9739   |
| 0.024         | 9.0   | 10503 | 0.1286          | 0.9226    | 0.9578 | 0.9399 | 0.9762   |
| 0.0209        | 10.0  | 11670 | 0.1297          | 0.9233    | 0.9608 | 0.9416 | 0.9764   |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1