Instructions to use hf-tiny-model-private/tiny-random-MPNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MPNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-MPNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
File size: 641 Bytes
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"_name_or_path": "tiny_models/mpnet/MPNetForSequenceClassification",
"architectures": [
"MPNetForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 64,
"initializer_range": 0.02,
"intermediate_size": 64,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "mpnet",
"num_attention_heads": 4,
"num_hidden_layers": 5,
"pad_token_id": 1,
"relative_attention_num_buckets": 32,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"vocab_size": 1125
}
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