Downloads · 30 days
3
18% of all-time downloads
mono_self_tig
Hellina/mono_self_tig
mono_self_tig is a machine learning model from Hellina. 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 apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
17
Public
Repo size
3.6 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors3.6 MB · 100%
At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Sep 28, 2024
- Updated
- Sep 28, 2024
- SHA
- 6504cf5a
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. -->
mono_self_tig
This model is a fine-tuned version of castorini/afriteva_v2_base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2721
- Accuracy: {'accuracy': 0.14912528216704288}
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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.9645 | 2.3810 | 100 | 2.6627 | {'accuracy': 0.11526523702031603} |
| 2.5095 | 4.7619 | 200 | 1.6713 | {'accuracy': 0.13896726862302483} |
| 1.9139 | 7.1429 | 300 | 1.5532 | {'accuracy': 0.14207110609480814} |
| 1.775 | 9.5238 | 400 | 1.4973 | {'accuracy': 0.14235327313769752} |
| 1.7046 | 11.9048 | 500 | 1.4564 | {'accuracy': 0.1433408577878104} |
| 1.6074 | 14.2857 | 600 | 1.4001 | {'accuracy': 0.14418735891647855} |
| 1.5531 | 16.6667 | 700 | 1.3427 | {'accuracy': 0.14672686230248308} |
| 1.4773 | 19.0476 | 800 | 1.3227 | {'accuracy': 0.14771444695259595} |
| 1.4325 | 21.4286 | 900 | 1.3025 | {'accuracy': 0.1488431151241535} |
| 1.4142 | 23.8095 | 1000 | 1.2946 | {'accuracy': 0.14813769751693} |
| 1.392 | 26.1905 | 1100 | 1.2749 | {'accuracy': 0.1488431151241535} |
| 1.3836 | 28.5714 | 1200 | 1.2721 | {'accuracy': 0.14912528216704288} |
Framework versions
- PEFT 0.7.1
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
- Library
- peft
- License
- apache-2.0
- Created
- Sep 28, 2024
- Updated
- Sep 28, 2024