Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use exala-e/db_himp_4.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use exala-e/db_himp_4.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="exala-e/db_himp_4.2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("exala-e/db_himp_4.2") model = AutoModelForSequenceClassification.from_pretrained("exala-e/db_himp_4.2") - Notebooks
- Google Colab
- Kaggle
File size: 2,068 Bytes
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"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.2,
"bos_token_id": null,
"dim": 768,
"dropout": 0.18,
"dtype": "float32",
"eos_token_id": null,
"hidden_dim": 3072,
"id2label": {
"0": "AD",
"1": "AM",
"2": "B",
"3": "BDNC",
"4": "BENE",
"5": "BN",
"6": "BOT",
"7": "CB",
"8": "CE",
"9": "COM",
"10": "CONF",
"11": "CQ",
"12": "DNC",
"13": "FD",
"14": "GNI",
"15": "H",
"16": "HOLD",
"17": "HOW",
"18": "HR",
"19": "HRN-",
"20": "LB",
"21": "N",
"22": "N-",
"23": "NE",
"24": "NG",
"25": "NO",
"26": "NQD",
"27": "OI",
"28": "P",
"29": "PCOST",
"30": "PG",
"31": "PG+",
"32": "PN",
"33": "PNI",
"34": "PO",
"35": "PONI",
"36": "POOR",
"37": "Q",
"38": "R",
"39": "RICH",
"40": "SCAM",
"41": "SEC",
"42": "TC",
"43": "TELE",
"44": "TMC",
"45": "U"
},
"initializer_range": 0.02,
"label2id": {
"AD": 0,
"AM": 1,
"B": 2,
"BDNC": 3,
"BENE": 4,
"BN": 5,
"BOT": 6,
"CB": 7,
"CE": 8,
"COM": 9,
"CONF": 10,
"CQ": 11,
"DNC": 12,
"FD": 13,
"GNI": 14,
"H": 15,
"HOLD": 16,
"HOW": 17,
"HR": 18,
"HRN-": 19,
"LB": 20,
"N": 21,
"N-": 22,
"NE": 23,
"NG": 24,
"NO": 25,
"NQD": 26,
"OI": 27,
"P": 28,
"PCOST": 29,
"PG": 30,
"PG+": 31,
"PN": 32,
"PNI": 33,
"PO": 34,
"PONI": 35,
"POOR": 36,
"Q": 37,
"R": 38,
"RICH": 39,
"SCAM": 40,
"SEC": 41,
"TC": 42,
"TELE": 43,
"TMC": 44,
"U": 45
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"tie_word_embeddings": true,
"transformers_version": "5.12.1",
"use_cache": false,
"vocab_size": 30522
}
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