Text Classification
Transformers
Safetensors
English
bert
distillation
eamkd
tinybert
text-embeddings-inference
Instructions to use HFTrails/Distilled-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HFTrails/Distilled-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HFTrails/Distilled-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HFTrails/Distilled-Model") model = AutoModelForSequenceClassification.from_pretrained("HFTrails/Distilled-Model") - Notebooks
- Google Colab
- Kaggle
File size: 751 Bytes
5f318cc | 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 | {
"_name_or_path": "./excluded_files/checkpoint/student_EAMKDLoss_LH_3470/checkpoint-epoch-9",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"cell": {},
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"pre_trained": "",
"structure": [],
"torch_dtype": "float32",
"transformers_version": "4.48.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
|