Downloads · 30 days
7
18% of all-time downloads
bertweetB_15epoch
dianamihalache27/bertweetB_15epoch
bertweetB_15epoch is a text classification model from dianamihalache27. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
39
Public
Parameters
135M
540 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors540 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Jun 6, 2024
- Updated
- Jun 6, 2024
- SHA
- 120fe4b5
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
bertweetB_15epoch
This model is a fine-tuned version of vinai/bertweet-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1645
- Accuracy: 0.77
- Precision: 0.2476
- Recall: 0.3173
- F1: 0.2757
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: 5e-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
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 217 | 0.1306 | 0.8571 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 434 | 0.1295 | 0.8571 | 0.0 | 0.0 | 0.0 |
| 0.1937 | 3.0 | 651 | 0.1268 | 0.8571 | 0.0 | 0.0 | 0.0 |
| 0.1937 | 4.0 | 868 | 0.1227 | 0.8593 | 0.3712 | 0.0701 | 0.1179 |
| 0.1473 | 5.0 | 1085 | 0.1307 | 0.765 | 0.2292 | 0.4354 | 0.3003 |
| 0.1473 | 6.0 | 1302 | 0.1270 | 0.7964 | 0.2457 | 0.3469 | 0.2877 |
| 0.1018 | 7.0 | 1519 | 0.1398 | 0.7607 | 0.2276 | 0.4354 | 0.2978 |
| 0.1018 | 8.0 | 1736 | 0.1449 | 0.7821 | 0.2323 | 0.3506 | 0.2786 |
| 0.1018 | 9.0 | 1953 | 0.1408 | 0.7843 | 0.2681 | 0.3764 | 0.3127 |
| 0.0648 | 10.0 | 2170 | 0.1535 | 0.78 | 0.2455 | 0.2878 | 0.2634 |
| 0.0648 | 11.0 | 2387 | 0.1585 | 0.7593 | 0.2375 | 0.3911 | 0.2954 |
| 0.0396 | 12.0 | 2604 | 0.1591 | 0.7757 | 0.2642 | 0.3100 | 0.2809 |
| 0.0396 | 13.0 | 2821 | 0.1670 | 0.7614 | 0.2347 | 0.3432 | 0.2774 |
| 0.0284 | 14.0 | 3038 | 0.1623 | 0.7793 | 0.2561 | 0.3026 | 0.2745 |
| 0.0284 | 15.0 | 3255 | 0.1645 | 0.77 | 0.2476 | 0.3173 | 0.2757 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Jun 6, 2024
- Updated
- Jun 6, 2024