Instructions to use Akhil06042002/model_model_super_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akhil06042002/model_model_super_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("Akhil06042002/model_model_super_model") model = AutoModelForMultipleChoice.from_pretrained("Akhil06042002/model_model_super_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Akhil06042002/model_model_super_model: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/Akhil06042002/model_model_super_model/resolve/main/README.md
- Command line
-
hf download hf://Akhil06042002/model_model_super_model/README.md
-
curl -L -o README.md https://huggingface.co/Akhil06042002/model_model_super_model/resolve/main/README.md
1.23 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: model_model_super_model
results: []
model_model_super_model
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 408 | 1.5653 | 0.2609 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3