Text Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
sagemaker
bertin
TextClassification
SentimentAnalysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use edumunozsala/bertin_base_sentiment_analysis_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edumunozsala/bertin_base_sentiment_analysis_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edumunozsala/bertin_base_sentiment_analysis_es")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edumunozsala/bertin_base_sentiment_analysis_es") model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/bertin_base_sentiment_analysis_es") - Notebooks
- Google Colab
- Kaggle
Commit ·
159a628
1
Parent(s): 09ed312
Fix Metric Typos (#1)
Browse files- Fix Metric Typos (bb7183bc2ad6de40a44f649553e41193fa65396f)
Co-authored-by: Matt Hyatt <mhyatt000@users.noreply.huggingface.co>
README.md
CHANGED
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@@ -20,17 +20,17 @@ model-index:
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name: "IMDb Reviews in Spanish"
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type: IMDbreviews_es
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.898933
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- name: F1 Score
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type: f1
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value: 0.8989063
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- name: Precision
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type: precision
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value: 0.8771473
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- name: Recall
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type: recall
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value: 0.9217724
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widget:
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- text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
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name: "IMDb Reviews in Spanish"
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type: IMDbreviews_es
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metrics:
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+
- name: Accuracy
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type: accuracy
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value: 0.898933
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| 26 |
+
- name: F1 Score
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| 27 |
+
type: f1
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value: 0.8989063
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| 29 |
+
- name: Precision
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type: precision
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value: 0.8771473
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| 32 |
+
- name: Recall
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type: recall
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value: 0.9217724
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widget:
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- text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
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