Downloads · 30 days
12
26% of all-time downloads
hbertv1-massive-logit_KD-tiny_ffn_2
gokuls/hbertv1-massive-logit_KD-tiny_ffn_2
hbertv1-massive-logit_KD-tiny_ffn_2 is a text classification model from gokuls. 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
46
Public
Parameters
4.3M
795 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors17.3 MB · 95%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- hybridbert
- Access
- Public
- Created
- Nov 26, 2023
- Updated
- Nov 26, 2023
- SHA
- dba2c54e
Try a prompt
Base models
Datasets
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. -->
hbertv1-massive-logit_KD-tiny_ffn_2
This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2 on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.6148
- Accuracy: 0.8313
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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.1929 | 1.0 | 180 | 3.5935 | 0.1402 |
| 3.4611 | 2.0 | 360 | 3.0049 | 0.2941 |
| 2.9024 | 3.0 | 540 | 2.4730 | 0.3792 |
| 2.4356 | 4.0 | 720 | 2.0721 | 0.4515 |
| 2.1041 | 5.0 | 900 | 1.8179 | 0.5278 |
| 1.8564 | 6.0 | 1080 | 1.6004 | 0.6257 |
| 1.6676 | 7.0 | 1260 | 1.4500 | 0.6596 |
| 1.5135 | 8.0 | 1440 | 1.3147 | 0.6995 |
| 1.3906 | 9.0 | 1620 | 1.2211 | 0.7147 |
| 1.2811 | 10.0 | 1800 | 1.1393 | 0.7314 |
| 1.1937 | 11.0 | 1980 | 1.0803 | 0.7304 |
| 1.112 | 12.0 | 2160 | 1.0267 | 0.7467 |
| 1.0488 | 13.0 | 2340 | 0.9716 | 0.7570 |
| 0.983 | 14.0 | 2520 | 0.9306 | 0.7649 |
| 0.9294 | 15.0 | 2700 | 0.8892 | 0.7767 |
| 0.8909 | 16.0 | 2880 | 0.8578 | 0.7885 |
| 0.8436 | 17.0 | 3060 | 0.8270 | 0.7909 |
| 0.8078 | 18.0 | 3240 | 0.8201 | 0.7964 |
| 0.7777 | 19.0 | 3420 | 0.7934 | 0.8028 |
| 0.7433 | 20.0 | 3600 | 0.7792 | 0.8037 |
| 0.7121 | 21.0 | 3780 | 0.7504 | 0.8082 |
| 0.6896 | 22.0 | 3960 | 0.7433 | 0.8091 |
| 0.6592 | 23.0 | 4140 | 0.7200 | 0.8160 |
| 0.6389 | 24.0 | 4320 | 0.7177 | 0.8096 |
| 0.6175 | 25.0 | 4500 | 0.7039 | 0.8136 |
| 0.6024 | 26.0 | 4680 | 0.6928 | 0.8180 |
| 0.5835 | 27.0 | 4860 | 0.6940 | 0.8170 |
| 0.5673 | 28.0 | 5040 | 0.6787 | 0.8136 |
| 0.5523 | 29.0 | 5220 | 0.6680 | 0.8229 |
| 0.5445 | 30.0 | 5400 | 0.6599 | 0.8234 |
| 0.5319 | 31.0 | 5580 | 0.6634 | 0.8214 |
| 0.5196 | 32.0 | 5760 | 0.6549 | 0.8259 |
| 0.504 | 33.0 | 5940 | 0.6506 | 0.8239 |
| 0.4993 | 34.0 | 6120 | 0.6518 | 0.8249 |
| 0.4941 | 35.0 | 6300 | 0.6388 | 0.8239 |
| 0.4823 | 36.0 | 6480 | 0.6317 | 0.8278 |
| 0.4734 | 37.0 | 6660 | 0.6327 | 0.8288 |
| 0.4609 | 38.0 | 6840 | 0.6312 | 0.8239 |
| 0.4617 | 39.0 | 7020 | 0.6279 | 0.8288 |
| 0.4529 | 40.0 | 7200 | 0.6255 | 0.8273 |
| 0.4491 | 41.0 | 7380 | 0.6173 | 0.8288 |
| 0.4419 | 42.0 | 7560 | 0.6148 | 0.8313 |
| 0.4378 | 43.0 | 7740 | 0.6208 | 0.8298 |
| 0.4362 | 44.0 | 7920 | 0.6140 | 0.8288 |
| 0.432 | 45.0 | 8100 | 0.6152 | 0.8308 |
| 0.4276 | 46.0 | 8280 | 0.6150 | 0.8288 |
| 0.4263 | 47.0 | 8460 | 0.6118 | 0.8308 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Created
- Nov 26, 2023
- Updated
- Nov 26, 2023