Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use LinusML/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinusML/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LinusML/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LinusML/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("LinusML/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,092 Bytes
42f75af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | {
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"bos_token_id": null,
"dim": 768,
"dropout": 0.1,
"dtype": "float32",
"eos_token_id": null,
"hidden_dim": 3072,
"id2label": {
"0": "A",
"1": "F",
"2": "FT",
"3": "L",
"4": "LF",
"5": "MN",
"6": "O",
"7": "PE",
"8": "PO",
"9": "SC",
"10": "SE",
"11": "US"
},
"initializer_range": 0.02,
"label2id": {
"A": 0,
"F": 1,
"FT": 2,
"L": 3,
"LF": 4,
"MN": 5,
"O": 6,
"PE": 7,
"PO": 8,
"SC": 9,
"SE": 10,
"US": 11
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"tie_word_embeddings": true,
"transformers_version": "5.18.0",
"use_cache": false,
"vocab_size": 30522
}
|