Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use KingTechnician/roberta-base_LOGIC_LRTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingTechnician/roberta-base_LOGIC_LRTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KingTechnician/roberta-base_LOGIC_LRTC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KingTechnician/roberta-base_LOGIC_LRTC") model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/roberta-base_LOGIC_LRTC") - Notebooks
- Google Colab
- Kaggle
File size: 954 Bytes
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"add_cross_attention": false,
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "C1",
"1": "C2",
"2": "C3",
"3": "C4",
"4": "F1",
"5": "S1",
"6": "S2",
"7": "S3"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
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"C2": 1,
"C3": 2,
"C4": 3,
"F1": 4,
"S1": 5,
"S2": 6,
"S3": 7
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"type_vocab_size": 1,
"use_cache": false,
"vocab_size": 50265
}
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