Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use tarnformnet/Stock-Sentiment-Bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarnformnet/Stock-Sentiment-Bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tarnformnet/Stock-Sentiment-Bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tarnformnet/Stock-Sentiment-Bert") model = AutoModelForSequenceClassification.from_pretrained("tarnformnet/Stock-Sentiment-Bert") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Stock-Sentiment-Bert
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This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an
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It achieves the following results on the evaluation set:
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## Model description
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## Intended uses & limitations
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# Stock-Sentiment-Bert
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This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an Stocktwits dataset.
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This dataset contain stocks related tweets which labelled as 'Bearish' and 'Bullish'.
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It achieves the following results on the evaluation set:
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## Model description
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First, trained the custom tokenizer from scratch using stocktwits dataset.
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Model is the fine tuned version of finbert model
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## Intended uses & limitations
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