Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-large-patch14-kinetics-600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-large-patch14-kinetics-600 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-large-patch14-kinetics-600")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-large-patch14-kinetics-600") model = AutoModel.from_pretrained("microsoft/xclip-large-patch14-kinetics-600", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": { | |
| "content": "<|startoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eos_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": "<|endoftext|>", | |
| "unk_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |