Downloads · 30 days
5
19% of all-time downloads
STS-Lora-Fine-Tuning-Capstone-bert-testing-22-with-lower-r
rajevan123/STS-Lora-Fine-Tuning-Capstone-bert-testing-22-with-lower-r
STS-Lora-Fine-Tuning-Capstone-bert-testing-22-with-lower-r is a machine learning model from rajevan123. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
27
Public
Repo size
13.9 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors764 KB · 46%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Mar 24, 2024
- Updated
- Mar 24, 2024
- SHA
- 35341420
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-Lora-Fine-Tuning-Capstone-bert-testing-22-with-lower-r
This model is a fine-tuned version of dslim/bert-base-NER on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4650
- Accuracy: 0.3843
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 | 180 | 1.7491 | 0.2429 |
| No log | 2.0 | 360 | 1.7398 | 0.2451 |
| 1.7057 | 3.0 | 540 | 1.7266 | 0.2408 |
| 1.7057 | 4.0 | 720 | 1.6996 | 0.2922 |
| 1.7057 | 5.0 | 900 | 1.6538 | 0.2988 |
| 1.6492 | 6.0 | 1080 | 1.6283 | 0.3118 |
| 1.6492 | 7.0 | 1260 | 1.5879 | 0.3270 |
| 1.6492 | 8.0 | 1440 | 1.5578 | 0.3387 |
| 1.5479 | 9.0 | 1620 | 1.5355 | 0.3503 |
| 1.5479 | 10.0 | 1800 | 1.5148 | 0.3561 |
| 1.5479 | 11.0 | 1980 | 1.5062 | 0.3561 |
| 1.4735 | 12.0 | 2160 | 1.5005 | 0.3691 |
| 1.4735 | 13.0 | 2340 | 1.4876 | 0.3843 |
| 1.437 | 14.0 | 2520 | 1.4799 | 0.3800 |
| 1.437 | 15.0 | 2700 | 1.4768 | 0.3785 |
| 1.437 | 16.0 | 2880 | 1.4732 | 0.3851 |
| 1.4223 | 17.0 | 3060 | 1.4689 | 0.3800 |
| 1.4223 | 18.0 | 3240 | 1.4684 | 0.3822 |
| 1.4223 | 19.0 | 3420 | 1.4657 | 0.3822 |
| 1.4123 | 20.0 | 3600 | 1.4650 | 0.3843 |
Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Library
- peft
- License
- mit
- Created
- Mar 24, 2024
- Updated
- Mar 24, 2024