Instructions to use textattack/bert-base-uncased-yelp-polarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-yelp-polarity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-yelp-polarity")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-yelp-polarity") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-yelp-polarity", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download config.json from textattack/bert-base-uncased-yelp-polarity: direct link, hf CLI and curl.
- Browser
- Download file 520 Bytes
-
https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/config.json
- Command line
-
hf download hf://textattack/bert-base-uncased-yelp-polarity/config.json
-
curl -L -o config.json https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/config.json
520 Bytes
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "finetuning_task": "yelp_polarity", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "type_vocab_size": 2, | |
| "vocab_size": 30522 | |
| } | |