Text Classification
Transformers
Safetensors
Turkish
bert
bist30
turkish
twitter
ner
text-embeddings-inference
Instructions to use engibeer/financial-ner-entities-bist30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use engibeer/financial-ner-entities-bist30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="engibeer/financial-ner-entities-bist30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("engibeer/financial-ner-entities-bist30") model = AutoModelForSequenceClassification.from_pretrained("engibeer/financial-ner-entities-bist30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22f005eed62db5798bff764920baab7f8aef15d29864450ccae768185aa92a1e
- Size of remote file:
- 737 MB
- SHA256:
- 91ad272f8c60b2aca09ee3d816b38390ad3630f1ad00453b335b0ae5faca1721
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