Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use shmagi/assignment3_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shmagi/assignment3_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shmagi/assignment3_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shmagi/assignment3_model") model = AutoModelForSequenceClassification.from_pretrained("shmagi/assignment3_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from shmagi/assignment3_model: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
-
https://huggingface.co/shmagi/assignment3_model/resolve/main/README.md
- Command line
-
hf download hf://shmagi/assignment3_model/README.md
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curl -L -o README.md https://huggingface.co/shmagi/assignment3_model/resolve/main/README.md
1.62 kB
| license: mit | |
| base_model: prajjwal1/bert-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: assignment3_model | |
| results: [] | |
| <!-- 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. --> | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/shmagi/huggingface/runs/ckwt5rwx) | |
| # assignment3_model | |
| This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3145 | |
| - Accuracy: 0.9112 | |
| ## 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: 2e-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: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.2632 | 1.0 | 4598 | 0.3168 | 0.9104 | | |
| | 0.2633 | 2.0 | 9196 | 0.3145 | 0.9112 | | |
| ### Framework versions | |
| - Transformers 4.41.0.dev0 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 | |