Text Classification
Transformers
Safetensors
bert
finance
twitter
prediction
ner
named-entity-recognition
turkish
text-embeddings-inference
Instructions to use engibeer/prediction-text-ner-bist30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use engibeer/prediction-text-ner-bist30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="engibeer/prediction-text-ner-bist30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("engibeer/prediction-text-ner-bist30") model = AutoModelForSequenceClassification.from_pretrained("engibeer/prediction-text-ner-bist30") - Notebooks
- Google Colab
- Kaggle
π§ Prediction Phrase Extractor (NER)
This is a fine-tuned Named Entity Recognition (NER) model that extracts stock prediction phrases from Turkish financial tweets. These prediction phrases are later passed into a sentiment classifier for further analysis.
π§Ύ Example predictions:
"will reach 70 TL""to moon soon""drop to 50 in 2 weeks"
π§ Model Details
- Developed by: damlakonur
- Model type:
BERTfine-tuned fortoken-classification - Language(s): Turkish
- Finetuned from:
bert-base-cased - Entity type:
Tahmin(prediction phrase) - License: MIT
π How to Use
from transformers import pipeline
model = pipeline(
"token-classification",
model="your-username/prediction-text-ner-bist30",
aggregation_strategy="simple"
)
model("EREGL will reach 45 TL in June.")
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Model tree for engibeer/prediction-text-ner-bist30
Base model
dbmdz/bert-base-turkish-128k-uncased