Instructions to use Labib11/PMC_GIST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Labib11/PMC_GIST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Labib11/PMC_GIST")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Labib11/PMC_GIST") model = AutoModel.from_pretrained("Labib11/PMC_GIST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Labib11/PMC_GIST: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/Labib11/PMC_GIST/resolve/main/README.md
- Command line
-
hf download hf://Labib11/PMC_GIST/README.md
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curl -L -o README.md https://huggingface.co/Labib11/PMC_GIST/resolve/main/README.md
1.26 kB
| license: mit | |
| base_model: avsolatorio/GIST-large-Embedding-v0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: PMC_GIST | |
| 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. --> | |
| # PMC_GIST | |
| This model is a fine-tuned version of [avsolatorio/GIST-large-Embedding-v0](https://huggingface.co/avsolatorio/GIST-large-Embedding-v0) 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: 1e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 4 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 64 | |
| - total_eval_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 40.0 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.40.1 | |
| - Pytorch 2.2.0+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 | |