Instructions to use slamos/bc-models-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slamos/bc-models-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slamos/bc-models-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slamos/bc-models-bert") model = AutoModelForSequenceClassification.from_pretrained("slamos/bc-models-bert") - Notebooks
- Google Colab
- Kaggle
File size: 415 Bytes
5db1e6b 94aa955 5db1e6b 94aa955 5db1e6b 94aa955 5db1e6b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"model_type": "bert",
"pretrained": "bert-base-uncased",
"epochs": 5,
"batch_size": 16,
"lr": 1e-05,
"max_length": 512,
"class_weights": [
1.0,
3.5319032669067383,
10.121383666992188
],
"num_labels": 3,
"id2label": {
"0": "SAME_PARAGRAPH",
"1": "NEW_PARAGRAPH",
"2": "NEWLINE"
},
"label2id": {
"SAME_PARAGRAPH": 0,
"NEW_PARAGRAPH": 1,
"NEWLINE": 2
}
} |