Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/DBERT_CleanDesc_Collision_v2.1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/DBERT_CleanDesc_Collision_v2.1.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/DBERT_CleanDesc_Collision_v2.1.2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/DBERT_CleanDesc_Collision_v2.1.2") model = AutoModelForSequenceClassification.from_pretrained("ratish/DBERT_CleanDesc_Collision_v2.1.2") - Notebooks
- Google Colab
- Kaggle
File size: 320 Bytes
9a6355d | 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": "DistilBertTokenizer",
"unk_token": "[UNK]"
}
|