Instructions to use davanstrien/clip-roberta-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davanstrien/clip-roberta-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="davanstrien/clip-roberta-finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("davanstrien/clip-roberta-finetuned") model = AutoModel.from_pretrained("davanstrien/clip-roberta-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from davanstrien/clip-roberta-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 962 Bytes
-
https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/README.md
- Command line
-
hf download hf://davanstrien/clip-roberta-finetuned@09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/README.md
-
curl -L -o README.md https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/README.md
962 Bytes
metadata
tags:
- generated_from_trainer
model-index:
- name: clip-roberta-finetuned
results: []
clip-roberta-finetuned
This model was trained from scratch on the None 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1