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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: CNEC1_1_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: 0.8266722759781236
- name: Recall
type: recall
value: 0.8815612382234186
- name: F1
type: f1
value: 0.8532349109856708
- name: Accuracy
type: accuracy
value: 0.961747140793942
---
<!-- 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. -->
# CNEC1_1_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: 0.2009
- Precision: 0.8267
- Recall: 0.8816
- F1: 0.8532
- Accuracy: 0.9617
## 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8866 | 0.85 | 500 | 0.2420 | 0.6729 | 0.7972 | 0.7298 | 0.9388 |
| 0.2429 | 1.7 | 1000 | 0.2028 | 0.7223 | 0.8331 | 0.7738 | 0.9491 |
| 0.1689 | 2.56 | 1500 | 0.1860 | 0.7606 | 0.8609 | 0.8077 | 0.9554 |
| 0.1427 | 3.41 | 2000 | 0.1791 | 0.7810 | 0.8672 | 0.8219 | 0.9548 |
| 0.1109 | 4.26 | 2500 | 0.1829 | 0.7876 | 0.8699 | 0.8267 | 0.9583 |
| 0.086 | 5.11 | 3000 | 0.2049 | 0.8042 | 0.8807 | 0.8407 | 0.9590 |
| 0.0706 | 5.96 | 3500 | 0.2008 | 0.8142 | 0.8730 | 0.8426 | 0.9600 |
| 0.0584 | 6.81 | 4000 | 0.1909 | 0.8253 | 0.8793 | 0.8514 | 0.9617 |
| 0.0512 | 7.67 | 4500 | 0.2009 | 0.8267 | 0.8816 | 0.8532 | 0.9617 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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