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:
- 8e70f98e4d08b73cebbaf3381aee82adbcaba4a9c11cd4d5ebd04a1c8603be8c
- Size of remote file:
- 885 MB
- SHA256:
- 37a47f8ac33103649c5c642352a2880339ca8922a2f53d5ce57b83c1d69a0184
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