Instructions to use bn22/experimental-te-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bn22/experimental-te-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bn22/experimental-te-ft")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bn22/experimental-te-ft") model = AutoModel.from_pretrained("bn22/experimental-te-ft") - Notebooks
- Google Colab
- Kaggle
File size: 536 Bytes
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"architectures": [
"CLIPTextModel"
],
"attention_dropout": 0.0,
"bos_token_id": 49406,
"dtype": "float32",
"eos_token_id": 49407,
"hidden_act": "gelu",
"hidden_size": 384,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 77,
"model_type": "clip_text_model",
"num_attention_heads": 8,
"num_hidden_layers": 12,
"pad_token_id": 1,
"projection_dim": 512,
"transformers_version": "5.0.0",
"vocab_size": 49408
}
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