File size: 3,084 Bytes
72fee6a |
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 |
---
license: apache-2.0
base_model: projecte-aina/roberta-base-ca-v2-cawikitc
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: stocks
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. -->
# stocks
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cawikitc](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cawikitc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6639
- Accuracy: 0.7637
- Precision: 0.5304
- Recall: 0.4710
- F1: 0.4778
- Ratio: 0.7903
## 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: 10
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- lr_scheduler_warmup_steps: 4
- num_epochs: 1
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.8468 | 0.07 | 10 | 0.8350 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 |
| 0.7922 | 0.14 | 20 | 0.8314 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 |
| 0.8005 | 0.21 | 30 | 0.8059 | 0.6169 | 0.2060 | 0.3325 | 0.2544 | 0.9984 |
| 0.8038 | 0.28 | 40 | 0.7907 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 |
| 0.7846 | 0.34 | 50 | 0.8060 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 |
| 0.7539 | 0.41 | 60 | 0.7573 | 0.6274 | 0.5024 | 0.3422 | 0.2763 | 0.9847 |
| 0.725 | 0.48 | 70 | 0.8018 | 0.7435 | 0.4978 | 0.4906 | 0.4940 | 0.5847 |
| 0.6842 | 0.55 | 80 | 0.8437 | 0.7419 | 0.5035 | 0.4795 | 0.4901 | 0.6444 |
| 0.7415 | 0.62 | 90 | 0.7783 | 0.7468 | 0.5006 | 0.4832 | 0.4909 | 0.6444 |
| 0.6303 | 0.69 | 100 | 0.7194 | 0.7452 | 0.5009 | 0.4723 | 0.4808 | 0.7040 |
| 0.6844 | 0.76 | 110 | 0.7137 | 0.7702 | 0.5106 | 0.4996 | 0.5044 | 0.6468 |
| 0.699 | 0.83 | 120 | 0.6666 | 0.7806 | 0.5159 | 0.5039 | 0.5084 | 0.6653 |
| 0.7229 | 0.9 | 130 | 0.6636 | 0.7629 | 0.5233 | 0.4730 | 0.4799 | 0.775 |
| 0.6555 | 0.97 | 140 | 0.6646 | 0.7637 | 0.5312 | 0.4707 | 0.4775 | 0.7919 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|