Instructions to use kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch20_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch20_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch20_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch20_v2") model = AutoModelForSequenceClassification.from_pretrained("kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch20_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 4
Browse files
model.safetensors
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