Text Classification
Transformers
Safetensors
English
bert
Generated from Trainer
sentiment_analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use cvnberk/crypto_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvnberk/crypto_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cvnberk/crypto_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cvnberk/crypto_sentiment") model = AutoModelForSequenceClassification.from_pretrained("cvnberk/crypto_sentiment") - Notebooks
- Google Colab
- Kaggle
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [ckandemir/bitcoin_tweets_sentiment_kaggle](https://huggingface.co/datasets/ckandemir/bitcoin_tweets_sentiment_kaggle) dataset.
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It achieves the following results on the evaluation set:
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# crypto_sentiment
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [ckandemir/bitcoin_tweets_sentiment_kaggle](https://huggingface.co/datasets/ckandemir/bitcoin_tweets_sentiment_kaggle) dataset.
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It achieves the following results on the evaluation set:
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