Text Classification
Transformers
PyTorch
Romanian
bert
sentiment
classification
romanian
nlp
Eval Results (legacy)
text-embeddings-inference
Instructions to use readerbench/ro-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/ro-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="readerbench/ro-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("readerbench/ro-sentiment") model = AutoModelForSequenceClassification.from_pretrained("readerbench/ro-sentiment") - Notebooks
- Google Colab
- Kaggle
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README.md
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- f1
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- f1 weighted
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model-index:
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- name: ro-sentiment
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results:
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- task:
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type: text-classification # Required. Example: automatic-speech-recognition
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# RO-Sentiment
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This model is a fine-tuned version of [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.3923
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- Accuracy: 0.8307
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- f1
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- f1 weighted
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model-index:
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- name: ro-sentiment
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results:
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- task:
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type: text-classification # Required. Example: automatic-speech-recognition
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# RO-Sentiment
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This model is a fine-tuned version of [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base) on the Decathlon reviews and Cinemagia reviews dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3923
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- Accuracy: 0.8307
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