Text Classification
Transformers
Safetensors
English
bert
nlp
classification
text-embeddings-inference
Instructions to use Kerassy/bert_base_tp_123k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kerassy/bert_base_tp_123k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kerassy/bert_base_tp_123k")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kerassy/bert_base_tp_123k") model = AutoModelForSequenceClassification.from_pretrained("Kerassy/bert_base_tp_123k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- google-bert/bert-base-uncased
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# Model Card for Kerassy/bert_base_tp_123k
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Classifies sentiment of user review text as either positive,
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- **Developed by:** Jay Broughton (Kerassy)
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- **Language(s) (NLP):** En
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- google-bert/bert-base-uncased
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# Model Card for Kerassy/bert_base_tp_123k
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Classifies sentiment of user review text as either positive, neutral or negative.
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- **Developed by:** Jay Broughton (Kerassy)
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- **Language(s) (NLP):** En
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