Text Classification
Transformers
PyTorch
TensorBoard
English
bert
Generated from Trainer
NLP Regression
Regression
Edmunds Car Reviews
text-embeddings-inference
Instructions to use DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews") - Notebooks
- Google Colab
- Kaggle
File size: 314 Bytes
13ce7e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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