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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: test
args: default
metrics:
- name: Precision
type: precision
value: 0.8282633808240277
- name: Recall
type: recall
value: 0.8837304847986853
- name: F1
type: f1
value: 0.8550983899821109
- name: Accuracy
type: accuracy
value: 0.9664021317268146
---
<!-- 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: 0.1865
- Precision: 0.8283
- Recall: 0.8837
- F1: 0.8551
- Accuracy: 0.9664
## 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
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 500
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6852 | 2.22 | 500 | 0.1614 | 0.7278 | 0.8250 | 0.7733 | 0.9574 |
| 0.1311 | 4.44 | 1000 | 0.1716 | 0.7690 | 0.8591 | 0.8116 | 0.9596 |
| 0.0882 | 6.67 | 1500 | 0.1785 | 0.7616 | 0.8714 | 0.8128 | 0.9613 |
| 0.062 | 8.89 | 2000 | 0.1536 | 0.8212 | 0.8928 | 0.8555 | 0.9669 |
| 0.0457 | 11.11 | 2500 | 0.1783 | 0.8204 | 0.8673 | 0.8432 | 0.9645 |
| 0.0353 | 13.33 | 3000 | 0.1829 | 0.8259 | 0.8809 | 0.8525 | 0.9655 |
| 0.0289 | 15.56 | 3500 | 0.1865 | 0.8283 | 0.8837 | 0.8551 | 0.9664 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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