Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ElMad/dapper-mouse-804 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElMad/dapper-mouse-804 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElMad/dapper-mouse-804")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElMad/dapper-mouse-804") model = AutoModelForSequenceClassification.from_pretrained("ElMad/dapper-mouse-804", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,221 Bytes
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"_name_or_path": "FacebookAI/roberta-base",
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "dictionary",
"1": "django",
"2": "list",
"3": "matplotlib",
"4": "numpy",
"5": "pandas",
"6": "python-2.7",
"7": "python-3.x",
"8": "regex",
"9": "string"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"dictionary": 0,
"django": 1,
"list": 2,
"matplotlib": 3,
"numpy": 4,
"pandas": 5,
"python-2.7": 6,
"python-3.x": 7,
"regex": 8,
"string": 9
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"problem_type": "multi_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.47.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
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