File size: 2,973 Bytes
139d41e f61fbea 139d41e ddde7ce 139d41e ddde7ce 139d41e ddde7ce 139d41e ddde7ce 139d41e ddde7ce 139d41e f61fbea 139d41e ddde7ce 139d41e ddde7ce 139d41e f61fbea ddde7ce 139d41e 4eca7d5 ddde7ce 139d41e |
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 |
---
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: 0.8236658932714617
- name: Recall
type: recall
value: 0.8751027115858668
- name: F1
type: f1
value: 0.848605577689243
- name: Accuracy
type: accuracy
value: 0.9646932746336094
---
<!-- 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: 0.2014
- Precision: 0.8237
- Recall: 0.8751
- F1: 0.8486
- Accuracy: 0.9647
## 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: 0.1
- lr_scheduler_warmup_steps: 1000
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9082 | 1.11 | 500 | 0.2281 | 0.6024 | 0.7539 | 0.6697 | 0.9424 |
| 0.1977 | 2.22 | 1000 | 0.1808 | 0.7211 | 0.8369 | 0.7747 | 0.9544 |
| 0.1477 | 3.33 | 1500 | 0.1674 | 0.7716 | 0.8661 | 0.8161 | 0.9612 |
| 0.1105 | 4.44 | 2000 | 0.1628 | 0.7860 | 0.8780 | 0.8294 | 0.9633 |
| 0.0929 | 5.56 | 2500 | 0.1609 | 0.7982 | 0.8743 | 0.8345 | 0.9629 |
| 0.0735 | 6.67 | 3000 | 0.1740 | 0.7901 | 0.8722 | 0.8291 | 0.9625 |
| 0.0614 | 7.78 | 3500 | 0.1860 | 0.8027 | 0.8710 | 0.8355 | 0.9641 |
| 0.0513 | 8.89 | 4000 | 0.1823 | 0.8038 | 0.8804 | 0.8404 | 0.9633 |
| 0.0399 | 10.0 | 4500 | 0.1866 | 0.8103 | 0.8846 | 0.8458 | 0.9639 |
| 0.0327 | 11.11 | 5000 | 0.2014 | 0.8237 | 0.8751 | 0.8486 | 0.9647 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|