Text Classification
Transformers
Safetensors
English
roberta
social-science
computational-social-science
situation-entity
genericity
discourse-modes
isaac
disco
Eval Results (legacy)
text-embeddings-inference
Instructions to use ISAAC-corpus/isaac-generalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ISAAC-corpus/isaac-generalization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ISAAC-corpus/isaac-generalization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ISAAC-corpus/isaac-generalization") model = AutoModelForSequenceClassification.from_pretrained("ISAAC-corpus/isaac-generalization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,003 Bytes
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"_name_or_path": "roberta-base",
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "BOUNDED EVENT (SPECIFIC)",
"1": "BOUNDED EVENT (GENERIC)",
"2": "UNBOUNDED EVENT (SPECIFIC)",
"3": "UNBOUNDED EVENT (GENERIC)",
"4": "BASIC STATE",
"5": "COERCED STATE (SPECIFIC)",
"6": "COERCED STATE (GENERIC)",
"7": "PERFECT COERCED STATE (SPECIFIC)",
"8": "PERFECT COERCED STATE (GENERIC)",
"9": "GENERIC SENTENCE (DYNAMIC)",
"10": "GENERIC SENTENCE (STATIC)",
"11": "GENERIC SENTENCE (HABITUAL)",
"12": "GENERALIZING SENTENCE (DYNAMIC)",
"13": "GENERALIZING SENTENCE (STATIVE)",
"14": "QUESTION",
"15": "IMPERATIVE",
"16": "NONSENSE",
"17": "OTHER"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"BOUNDED EVENT (SPECIFIC)": 0,
"BOUNDED EVENT (GENERIC)": 1,
"UNBOUNDED EVENT (SPECIFIC)": 2,
"UNBOUNDED EVENT (GENERIC)": 3,
"BASIC STATE": 4,
"COERCED STATE (SPECIFIC)": 5,
"COERCED STATE (GENERIC)": 6,
"PERFECT COERCED STATE (SPECIFIC)": 7,
"PERFECT COERCED STATE (GENERIC)": 8,
"GENERIC SENTENCE (DYNAMIC)": 9,
"GENERIC SENTENCE (STATIC)": 10,
"GENERIC SENTENCE (HABITUAL)": 11,
"GENERALIZING SENTENCE (DYNAMIC)": 12,
"GENERALIZING SENTENCE (STATIVE)": 13,
"QUESTION": 14,
"IMPERATIVE": 15,
"NONSENSE": 16,
"OTHER": 17
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.24.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
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