File size: 1,996 Bytes
e38b33b 3bff8e0 e38b33b 8b2d43a e38b33b 3bff8e0 e38b33b 8b2d43a e38b33b |
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 |
---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- f1
- recall
model-index:
- name: bert-base-multilingual-cased
results: []
---
<!-- 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. -->
# bert-base-multilingual-cased
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5680
- F1 Macro: 0.8376
- F1: 0.8868
- F1 Neg: 0.7885
- Acc: 0.8525
- Prec: 0.8619
- Recall: 0.9130
- Mcc: 0.6781
## 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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:-----:|:------:|:------:|:------:|
| 0.6283 | 1.0 | 857 | 0.5262 | 0.7053 | 0.8379 | 0.5727 | 0.765 | 0.7454 | 0.9567 | 0.4813 |
| 0.5741 | 2.0 | 1714 | 0.5939 | 0.8028 | 0.8610 | 0.7447 | 0.82 | 0.8447 | 0.8780 | 0.6069 |
| 0.4751 | 3.0 | 2571 | 0.6656 | 0.8198 | 0.8801 | 0.7594 | 0.84 | 0.8393 | 0.9252 | 0.6482 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|