Instructions to use ninagroot/thesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ninagroot/thesis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ninagroot/thesis", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ninagroot/thesis", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("ninagroot/thesis", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,112 Bytes
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"_name_or_path": "microsoft/phi-1_5",
"architectures": [
"PhiForSequenceClassification"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "microsoft/phi-1_5--configuration_phi.PhiConfig",
"AutoModelForCausalLM": "microsoft/phi-1_5--modeling_phi.PhiForCausalLM"
},
"bos_token_id": null,
"embd_pdrop": 0.0,
"eos_token_id": null,
"hidden_act": "gelu_new",
"hidden_size": 2048,
"id2label": {
"0": "negative",
"1": "positive"
},
"initializer_range": 0.02,
"intermediate_size": 8192,
"label2id": {
"negative": "0",
"positive": "1"
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 2048,
"model_type": "phi",
"num_attention_heads": 32,
"num_hidden_layers": 24,
"num_key_value_heads": 32,
"pad_token_id": 50256,
"partial_rotary_factor": 0.5,
"problem_type": "single_label_classification",
"qk_layernorm": false,
"resid_pdrop": 0.0,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.37.1",
"use_cache": true,
"vocab_size": 51200
}
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