Instructions to use HyperlinksSpace/TinyModel1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HyperlinksSpace/TinyModel1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HyperlinksSpace/TinyModel1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HyperlinksSpace/TinyModel1") model = AutoModelForSequenceClassification.from_pretrained("HyperlinksSpace/TinyModel1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 773 Bytes
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"name": "TinyModel1",
"version": "0.3.0",
"task": "text-classification",
"dataset": "fancyzhx/ag_news",
"dataset_config": null,
"train_split": "train",
"eval_split": "test",
"text_column": "text",
"label_column": "label",
"base_model": "tinymodel1-bert-scratch",
"labels": [
"World",
"Sports",
"Business",
"Sci/Tech"
],
"eval_accuracy": 0.5383,
"eval_macro_f1": 0.4554,
"eval_weighted_f1": 0.4527,
"eval_per_class_f1": {
"World": 0.5366,
"Sports": 0.731,
"Business": 0.0,
"Sci/Tech": 0.5539
},
"train_loss": 1.1567,
"num_parameters": 1339268,
"max_train_samples": 3000,
"max_eval_samples": 600,
"max_seq_length": 128,
"epochs": 2,
"batch_size": 16,
"learning_rate": 0.0001,
"seed": 42
}
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