Instructions to use semaj83/ctmatch-clf-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use semaj83/ctmatch-clf-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="semaj83/ctmatch-clf-R")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("semaj83/ctmatch-clf-R") model = AutoModelForSequenceClassification.from_pretrained("semaj83/ctmatch-clf-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,005 Bytes
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"add_cross_attention": false,
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "not_relevant",
"1": "partially_relevant",
"2": "relevant"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"label2id": {
"not_relevant": 0,
"partially_relevant": 1,
"relevant": 2
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"tie_word_embeddings": true,
"transformers_version": "5.12.1",
"type_vocab_size": 2,
"use_cache": false,
"vocab_size": 28895
}
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