Text Classification
Transformers
PyTorch
Turkish
bert
deprem-clf-v1
Eval Results (legacy)
text-embeddings-inference
Instructions to use deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased") model = AutoModelForSequenceClassification.from_pretrained("deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6a93f76d1e05f30a159047db7a1fd8c74d49879b5aeeeacdafb295f22c22a91
- Size of remote file:
- 627 Bytes
- SHA256:
- fa0ed8d002b2243afe806ae75b93cda8eded403655be119f18957bb332a19459
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