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---
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: 0.8615819209039548
- name: Recall
type: recall
value: 0.8818669971086328
- name: F1
type: f1
value: 0.8716064502959787
- name: Accuracy
type: accuracy
value: 0.9709691438504998
---
<!-- 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: 0.1178
- Precision: 0.8616
- Recall: 0.8819
- F1: 0.8716
- Accuracy: 0.9710
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 225 | 0.1357 | 0.7953 | 0.8315 | 0.8130 | 0.9620 |
| No log | 2.0 | 450 | 0.1056 | 0.8245 | 0.8691 | 0.8462 | 0.9687 |
| 0.21 | 3.0 | 675 | 0.1064 | 0.8487 | 0.8831 | 0.8656 | 0.9698 |
| 0.21 | 4.0 | 900 | 0.1198 | 0.8442 | 0.8839 | 0.8636 | 0.9704 |
| 0.0589 | 5.0 | 1125 | 0.1178 | 0.8616 | 0.8819 | 0.8716 | 0.9710 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|