Text Classification
Transformers
PyTorch
Safetensors
bert
voicemail-detection
text-embeddings-inference
Instructions to use Adya662/bert-tiny-amd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adya662/bert-tiny-amd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Adya662/bert-tiny-amd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Adya662/bert-tiny-amd") model = AutoModelForSequenceClassification.from_pretrained("Adya662/bert-tiny-amd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 694 Bytes
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"model_type": "bert",
"architectures": [
"BertForSequenceClassification"
],
"attention_proxy_dtype": "float32",
"attention_dropout": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 128,
"initializer_range": 0.02,
"intermediate_size": 512,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"num_attention_heads": 2,
"num_hidden_layers": 2,
"num_labels": 1,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.21.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
} |