Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use institutional/institutional-books-topic-classifier-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use institutional/institutional-books-topic-classifier-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="institutional/institutional-books-topic-classifier-bert", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("institutional/institutional-books-topic-classifier-bert") model = AutoModelForSequenceClassification.from_pretrained("institutional/institutional-books-topic-classifier-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update training_params.json
Browse files- training_params.json +2 -2
training_params.json
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{
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"data_path": "
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"model": "google-bert/bert-base-multilingual-uncased",
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"lr": 5e-05,
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"epochs": 3,
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"text_column": "autotrain_text",
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"target_column": "autotrain_label",
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"logging_steps": -1,
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"project_name": "
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"auto_find_batch_size": false,
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"mixed_precision": "fp16",
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"save_total_limit": 1,
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{
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"data_path": "institutional-books-topic-classifier-bert/autotrain-data",
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"model": "google-bert/bert-base-multilingual-uncased",
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"lr": 5e-05,
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"epochs": 3,
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"text_column": "autotrain_text",
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"target_column": "autotrain_label",
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"logging_steps": -1,
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"project_name": "institutional-books-topic-classifier-bert",
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"auto_find_batch_size": false,
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"mixed_precision": "fp16",
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"save_total_limit": 1,
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