Instructions to use mattbit/distilbert-tweet-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mattbit/distilbert-tweet-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mattbit/distilbert-tweet-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mattbit/distilbert-tweet-sentiment") model = AutoModelForSequenceClassification.from_pretrained("mattbit/distilbert-tweet-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ee06ed581053f5189de6001b86f1afbbd24b5beb935816a4834333fee0c8f76
- Size of remote file:
- 263 MB
- SHA256:
- 4bcde3f1ff7c1daaeef7f64964865748efd78f08360a73e4a8e6f46403c3320c
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