Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use eskayML/bert_combined_top_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eskayML/bert_combined_top_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eskayML/bert_combined_top_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eskayML/bert_combined_top_2") model = AutoModelForSequenceClassification.from_pretrained("eskayML/bert_combined_top_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ee2060000753d09564d55777cea736e360b0feb6f489cd4e39092b7dde0ab7d
- Size of remote file:
- 5.3 kB
- SHA256:
- 1a190219e27149ff198669dd48127e8a94c36ef910b8e6c29e99dfe3ab1f0659
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