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---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
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: 0.7557829181494662
- name: Recall
type: recall
value: 0.819980694980695
- name: F1
type: f1
value: 0.7865740740740742
- name: Accuracy
type: accuracy
value: 0.9568269568269568
---
<!-- 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 [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2049
- Precision: 0.7558
- Recall: 0.8200
- F1: 0.7866
- Accuracy: 0.9568
## 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
- lr_scheduler_warmup_ratio: 0.01
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7025 | 1.11 | 500 | 0.2950 | 0.5066 | 0.5927 | 0.5463 | 0.9128 |
| 0.2152 | 2.22 | 1000 | 0.2057 | 0.6733 | 0.7539 | 0.7113 | 0.9425 |
| 0.1366 | 3.33 | 1500 | 0.1680 | 0.7228 | 0.7891 | 0.7545 | 0.9525 |
| 0.0849 | 4.44 | 2000 | 0.1710 | 0.7246 | 0.7987 | 0.7599 | 0.9540 |
| 0.0574 | 5.56 | 2500 | 0.1725 | 0.7309 | 0.8166 | 0.7714 | 0.9558 |
| 0.0384 | 6.67 | 3000 | 0.1855 | 0.7327 | 0.8243 | 0.7758 | 0.9554 |
| 0.0292 | 7.78 | 3500 | 0.1944 | 0.7557 | 0.8287 | 0.7905 | 0.9573 |
| 0.0208 | 8.89 | 4000 | 0.2053 | 0.7486 | 0.8118 | 0.7789 | 0.9555 |
| 0.0164 | 10.0 | 4500 | 0.2049 | 0.7558 | 0.8200 | 0.7866 | 0.9568 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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