Instructions to use Kanit/bert-hateXplain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kanit/bert-hateXplain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kanit/bert-hateXplain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kanit/bert-hateXplain") model = AutoModelForSequenceClassification.from_pretrained("Kanit/bert-hateXplain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 822 Bytes
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"_name_or_path": "google/bert_uncased_L-4_H-256_A-4",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 256,
"id2label": {
"0": "non-toxic",
"1": "toxic"
},
"initializer_range": 0.02,
"intermediate_size": 1024,
"label2id": {
"non-toxic": 0,
"toxic": 1
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 4,
"num_hidden_layers": 4,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.34.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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