File size: 2,896 Bytes
0257f5c | 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 | ---
license: apache-2.0
base_model: projecte-aina/roberta-base-ca-v2-cased-te
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-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6733
- Accuracy: 0.8109
- Precision: 0.8127
- Recall: 0.8109
- F1: 0.8107
- Ratio: 0.5378
## 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: 2
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 3.8156 | 0.1626 | 10 | 1.9553 | 0.5378 | 0.5507 | 0.5378 | 0.5064 | 0.7521 |
| 1.3339 | 0.3252 | 20 | 1.2090 | 0.5546 | 0.5548 | 0.5546 | 0.5543 | 0.5252 |
| 1.103 | 0.4878 | 30 | 0.9577 | 0.5588 | 0.5588 | 0.5588 | 0.5588 | 0.5042 |
| 0.9108 | 0.6504 | 40 | 0.8881 | 0.5714 | 0.5770 | 0.5714 | 0.5635 | 0.6345 |
| 0.8716 | 0.8130 | 50 | 0.8426 | 0.6387 | 0.6563 | 0.6387 | 0.6282 | 0.6681 |
| 0.844 | 0.9756 | 60 | 0.7948 | 0.7017 | 0.7233 | 0.7017 | 0.6943 | 0.3445 |
| 0.7816 | 1.1382 | 70 | 0.7715 | 0.7227 | 0.7660 | 0.7227 | 0.7109 | 0.7017 |
| 0.7406 | 1.3008 | 80 | 0.7040 | 0.8067 | 0.8099 | 0.8067 | 0.8062 | 0.5504 |
| 0.6764 | 1.4634 | 90 | 0.6954 | 0.8025 | 0.8104 | 0.8025 | 0.8013 | 0.5798 |
| 0.7306 | 1.6260 | 100 | 0.6933 | 0.8109 | 0.8209 | 0.8109 | 0.8094 | 0.5882 |
| 0.6736 | 1.7886 | 110 | 0.6763 | 0.8067 | 0.8089 | 0.8067 | 0.8064 | 0.5420 |
| 0.714 | 1.9512 | 120 | 0.6733 | 0.8109 | 0.8127 | 0.8109 | 0.8107 | 0.5378 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|