Instructions to use Narsil/tiny-distilbert-sequence-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/tiny-distilbert-sequence-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Narsil/tiny-distilbert-sequence-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/tiny-distilbert-sequence-classification") model = AutoModelForSequenceClassification.from_pretrained("Narsil/tiny-distilbert-sequence-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 590 Bytes
309cd45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_name_or_path": "/home/nicolas/data/weights/distilbert/distil_bert_for_sequence_classification",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 32,
"dropout": 0.1,
"hidden_act": "gelu",
"hidden_dim": 37,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 4,
"n_layers": 5,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.10.0.dev0",
"vocab_size": 99
}
|