Instructions to use DunnBC22/bertweet-base-Twitter_Sentiment_Analysis_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/bertweet-base-Twitter_Sentiment_Analysis_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/bertweet-base-Twitter_Sentiment_Analysis_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/bertweet-base-Twitter_Sentiment_Analysis_v3") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/bertweet-base-Twitter_Sentiment_Analysis_v3") - Notebooks
- Google Colab
- Kaggle
File size: 379 Bytes
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"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"mask_token": "<mask>",
"model_max_length": 128,
"name_or_path": "vinai/bertweet-base",
"normalization": false,
"pad_token": "<pad>",
"padding": true,
"sep_token": "</s>",
"special_tokens_map_file": null,
"tokenizer_class": "BertweetTokenizer",
"truncation": true,
"unk_token": "<unk>"
}
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