Text Classification
Transformers
Safetensors
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use pszemraj/xtremedistil-l6-h256-OCR-quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/xtremedistil-l6-h256-OCR-quality with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pszemraj/xtremedistil-l6-h256-OCR-quality")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pszemraj/xtremedistil-l6-h256-OCR-quality") model = AutoModelForSequenceClassification.from_pretrained("pszemraj/xtremedistil-l6-h256-OCR-quality", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 898 Bytes
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"_name_or_path": "microsoft/xtremedistil-l6-h256-uncased",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"finetuning_task": "text-classification",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 256,
"id2label": {
"0": "clean",
"1": "noisy"
},
"initializer_range": 0.02,
"intermediate_size": 1024,
"label2id": {
"clean": 0,
"noisy": 1
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 8,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.40.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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