---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC2_0_Supertypes_xlm-roberta-large
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cnec
      type: cnec
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8427382053654024
    - name: Recall
      type: recall
      value: 0.8793436293436293
    - name: F1
      type: f1
      value: 0.8606518658478979
    - name: Accuracy
      type: accuracy
      value: 0.9671736925974214
---

<!-- 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. -->

# CNEC2_0_Supertypes_xlm-roberta-large

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2674
- Precision: 0.8427
- Recall: 0.8793
- F1: 0.8607
- Accuracy: 0.9672

## 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: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5221        | 1.11  | 500   | 0.1718          | 0.6648    | 0.8012 | 0.7266 | 0.9535   |
| 0.1777        | 2.22  | 1000  | 0.1397          | 0.7499    | 0.8393 | 0.7921 | 0.9627   |
| 0.1321        | 3.33  | 1500  | 0.1383          | 0.7760    | 0.8711 | 0.8208 | 0.9655   |
| 0.1132        | 4.44  | 2000  | 0.1456          | 0.7646    | 0.8542 | 0.8069 | 0.9636   |
| 0.1008        | 5.56  | 2500  | 0.1442          | 0.7750    | 0.8692 | 0.8194 | 0.9648   |
| 0.0782        | 6.67  | 3000  | 0.1516          | 0.8107    | 0.8663 | 0.8376 | 0.9657   |
| 0.0692        | 7.78  | 3500  | 0.1690          | 0.8023    | 0.8620 | 0.8311 | 0.9660   |
| 0.0582        | 8.89  | 4000  | 0.1591          | 0.8125    | 0.8847 | 0.8470 | 0.9672   |
| 0.0511        | 10.0  | 4500  | 0.1813          | 0.8033    | 0.8832 | 0.8414 | 0.9661   |
| 0.0432        | 11.11 | 5000  | 0.1833          | 0.8231    | 0.8822 | 0.8516 | 0.9669   |
| 0.0381        | 12.22 | 5500  | 0.2097          | 0.8062    | 0.8634 | 0.8338 | 0.9659   |
| 0.0328        | 13.33 | 6000  | 0.2043          | 0.8026    | 0.8711 | 0.8355 | 0.9661   |
| 0.0292        | 14.44 | 6500  | 0.2217          | 0.8255    | 0.8769 | 0.8505 | 0.9669   |
| 0.0247        | 15.56 | 7000  | 0.2411          | 0.8297    | 0.8745 | 0.8515 | 0.9667   |
| 0.0206        | 16.67 | 7500  | 0.2425          | 0.8255    | 0.8764 | 0.8502 | 0.9663   |
| 0.0184        | 17.78 | 8000  | 0.2405          | 0.8329    | 0.8586 | 0.8455 | 0.9668   |
| 0.0157        | 18.89 | 8500  | 0.2521          | 0.8314    | 0.8832 | 0.8565 | 0.9677   |
| 0.0134        | 20.0  | 9000  | 0.2504          | 0.8349    | 0.8764 | 0.8552 | 0.9671   |
| 0.0116        | 21.11 | 9500  | 0.2570          | 0.8344    | 0.8779 | 0.8556 | 0.9678   |
| 0.0109        | 22.22 | 10000 | 0.2570          | 0.8320    | 0.8793 | 0.8550 | 0.9677   |
| 0.0093        | 23.33 | 10500 | 0.2639          | 0.8373    | 0.8793 | 0.8578 | 0.9674   |
| 0.0086        | 24.44 | 11000 | 0.2674          | 0.8427    | 0.8793 | 0.8607 | 0.9672   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0