Downloads · 30 days
8
15% of all-time downloads
STS-conventional-Fine-Tuning-Capstone-roberta-base-filtered-160
rajevan123/STS-conventional-Fine-Tuning-Capstone-roberta-base-filtered-160
STS-conventional-Fine-Tuning-Capstone-roberta-base-filtered-160 is a text classification model from rajevan123. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
53
Public
Parameters
125M
8 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors499 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Apr 1, 2024
- Updated
- Apr 1, 2024
- SHA
- e8975c40
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. -->
STS-conventional-Fine-Tuning-Capstone-roberta-base-filtered-160
This model is a fine-tuned version of FacebookAI/roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2762
- Accuracy: 0.7247
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 113 | 0.7940 | 0.6610 |
| No log | 2.0 | 226 | 0.7463 | 0.6929 |
| No log | 3.0 | 339 | 0.9240 | 0.7041 |
| No log | 4.0 | 452 | 0.9070 | 0.6629 |
| 0.5167 | 5.0 | 565 | 1.1376 | 0.7022 |
| 0.5167 | 6.0 | 678 | 1.2043 | 0.7022 |
| 0.5167 | 7.0 | 791 | 1.3083 | 0.7228 |
| 0.5167 | 8.0 | 904 | 1.5205 | 0.7154 |
| 0.1626 | 9.0 | 1017 | 1.5875 | 0.7154 |
| 0.1626 | 10.0 | 1130 | 1.8172 | 0.7041 |
| 0.1626 | 11.0 | 1243 | 1.9300 | 0.7154 |
| 0.1626 | 12.0 | 1356 | 1.8632 | 0.7247 |
| 0.1626 | 13.0 | 1469 | 2.0908 | 0.7135 |
| 0.0655 | 14.0 | 1582 | 2.0766 | 0.7191 |
| 0.0655 | 15.0 | 1695 | 2.2582 | 0.7135 |
| 0.0655 | 16.0 | 1808 | 2.2743 | 0.7154 |
| 0.0655 | 17.0 | 1921 | 2.2310 | 0.7228 |
| 0.0237 | 18.0 | 2034 | 2.2574 | 0.7285 |
| 0.0237 | 19.0 | 2147 | 2.2768 | 0.7266 |
| 0.0237 | 20.0 | 2260 | 2.2762 | 0.7247 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Apr 1, 2024
- Updated
- Apr 1, 2024