File size: 3,198 Bytes
91af60b 866c6d5 91af60b 866c6d5 91af60b 866c6d5 91af60b 866c6d5 91af60b 866c6d5 91af60b 866c6d5 91af60b 866c6d5 91af60b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC2_0_extended_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: 0.8860882210373243
- name: Recall
type: recall
value: 0.9071960297766749
- name: F1
type: f1
value: 0.8965179009318294
- name: Accuracy
type: accuracy
value: 0.9772540983606557
---
<!-- 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_extended_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: 0.1919
- Precision: 0.8861
- Recall: 0.9072
- F1: 0.8965
- Accuracy: 0.9773
## 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
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1664 | 1.12 | 1000 | 0.1312 | 0.8299 | 0.8521 | 0.8408 | 0.9695 |
| 0.1153 | 2.24 | 2000 | 0.1121 | 0.8283 | 0.8640 | 0.8458 | 0.9722 |
| 0.0815 | 3.36 | 3000 | 0.1159 | 0.8523 | 0.8531 | 0.8527 | 0.9735 |
| 0.0633 | 4.48 | 4000 | 0.1166 | 0.8515 | 0.8819 | 0.8664 | 0.9750 |
| 0.0472 | 5.6 | 5000 | 0.1624 | 0.8635 | 0.8918 | 0.8774 | 0.9735 |
| 0.0369 | 6.72 | 6000 | 0.1476 | 0.8710 | 0.8983 | 0.8844 | 0.9770 |
| 0.0325 | 7.84 | 7000 | 0.1590 | 0.8710 | 0.8943 | 0.8825 | 0.9752 |
| 0.0268 | 8.96 | 8000 | 0.1698 | 0.8709 | 0.9037 | 0.8870 | 0.9761 |
| 0.0236 | 10.08 | 9000 | 0.1721 | 0.8807 | 0.9087 | 0.8945 | 0.9763 |
| 0.0125 | 11.2 | 10000 | 0.1843 | 0.8781 | 0.9047 | 0.8912 | 0.9768 |
| 0.009 | 12.32 | 11000 | 0.1971 | 0.8789 | 0.9077 | 0.8931 | 0.9766 |
| 0.0097 | 13.44 | 12000 | 0.1823 | 0.8857 | 0.9077 | 0.8966 | 0.9775 |
| 0.0077 | 14.56 | 13000 | 0.1919 | 0.8861 | 0.9072 | 0.8965 | 0.9773 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|