---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-ner-en
  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. -->

# roberta-finetuned-ner-en

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Erson B: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 296}
- Erson I: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 296}
- Oc B: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 505}
- Oc I: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 505}
- Roduct B: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57}
- Roduct I: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57}
- Vent B: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 62}
- Vent I: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 77}
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Erson B                                                     | Erson I                                                                                                 | Oc B                                                                                                    | Oc I                                                                                                    | Roduct B                                                   | Roduct I                                                   | Vent B                                                                                   | Vent I                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log        | 1.0   | 300  | 0.0442          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 296} | {'precision': 0.9473684210526315, 'recall': 0.972972972972973, 'f1': 0.9599999999999999, 'number': 296} | {'precision': 0.9153225806451613, 'recall': 0.899009900990099, 'f1': 0.9070929070929071, 'number': 505} | {'precision': 0.9560669456066946, 'recall': 0.904950495049505, 'f1': 0.9298067141403866, 'number': 505} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57} | {'precision': 0.9841269841269841, 'recall': 1.0, 'f1': 0.9919999999999999, 'number': 62} | {'precision': 1.0, 'recall': 0.987012987012987, 'f1': 0.9934640522875817, 'number': 77} | 0.9562            | 0.9418         | 0.9489     | 0.9862           |
| 0.2261        | 2.0   | 600  | 0.0001          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 296} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 296}                                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 505}                                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 505}                                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 62}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 77}                              | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.2261        | 3.0   | 900  | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 296} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 296}                                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 505}                                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 505}                                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 57} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 62}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 77}                              | 1.0               | 1.0            | 1.0        | 1.0              |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3