Instructions to use tner/bert-base-tweetner7-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/bert-base-tweetner7-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/bert-base-tweetner7-random")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/bert-base-tweetner7-random") model = AutoModelForTokenClassification.from_pretrained("tner/bert-base-tweetner7-random") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74132352e3b0cb2d65e67d55096102ec4f011c3f4ebbe28e3b787d8821c9434d
- Size of remote file:
- 431 MB
- SHA256:
- 65a19bb5514a1335e47e8a5efc066607f32cf04b3b8ef2c0f7d721e8faf8eb7c
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