Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use sherlzanora/hs_multilabel_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sherlzanora/hs_multilabel_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sherlzanora/hs_multilabel_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sherlzanora/hs_multilabel_bert") model = AutoModelForSequenceClassification.from_pretrained("sherlzanora/hs_multilabel_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hs_multilabel_bert
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.46.3
- TensorFlow 2.17.1
- Datasets 3.1.0
- Tokenizers 0.20.3
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