Downloads · 30 days
5
2% of all-time downloads
muril-base-cased-finetuned-combined-DS
IIIT-L/muril-base-cased-finetuned-combined-DS
muril-base-cased-finetuned-combined-DS is a text classification model from IIIT-L. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
316
Public
Repo size
12.4 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin950 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Sep 29, 2022
- Updated
- Sep 29, 2022
- SHA
- b1c3aa99
Try a prompt
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. -->
muril-base-cased-finetuned-combined-DS
This model is a fine-tuned version of google/muril-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5291
- Accuracy: 0.6657
- Precision: 0.6355
- Recall: 0.6275
- F1: 0.6294
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.9961 | 2.0 | 711 | 0.9148 | 0.5625 | 0.5495 | 0.5636 | 0.5265 |
| 0.8211 | 3.99 | 1422 | 0.8542 | 0.6096 | 0.6023 | 0.6071 | 0.5928 |
| 0.6667 | 5.99 | 2133 | 0.8459 | 0.6601 | 0.6366 | 0.6379 | 0.6361 |
| 0.5272 | 7.99 | 2844 | 0.9667 | 0.6517 | 0.6190 | 0.6223 | 0.6201 |
| 0.4327 | 9.99 | 3555 | 1.0185 | 0.6503 | 0.6351 | 0.6222 | 0.6229 |
| 0.3608 | 11.98 | 4266 | 1.1409 | 0.6313 | 0.6053 | 0.6100 | 0.6049 |
| 0.3038 | 13.98 | 4977 | 1.2336 | 0.6601 | 0.6287 | 0.6269 | 0.6273 |
| 0.2631 | 15.98 | 5688 | 1.3151 | 0.6503 | 0.6199 | 0.6167 | 0.6177 |
| 0.2368 | 17.97 | 6399 | 1.4230 | 0.6594 | 0.6315 | 0.6233 | 0.6251 |
| 0.2093 | 19.97 | 7110 | 1.4881 | 0.6629 | 0.6332 | 0.6220 | 0.6239 |
| 0.1968 | 21.97 | 7821 | 1.5003 | 0.6559 | 0.6279 | 0.6230 | 0.6242 |
| 0.1824 | 23.97 | 8532 | 1.5291 | 0.6657 | 0.6355 | 0.6275 | 0.6294 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Sep 29, 2022
- Updated
- Sep 29, 2022