Text Classification
Transformers
English
sentiment-analysis
classification
from-scratch
Eval Results (legacy)
Instructions to use LH-Tech-AI/CritiqueCore_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LH-Tech-AI/CritiqueCore_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LH-Tech-AI/CritiqueCore_v1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LH-Tech-AI/CritiqueCore_v1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -56,28 +56,21 @@ Use `inference.py` from this repos' files list. Have fun :D
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```plaintext
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This movie was an absolute masterpiece! The acting was incredible and I loved every second.
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```
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**Output:**
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POSITIVE (99.03% confidence)
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```
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### Example 2: Sarcasm
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**Input:**
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```plaintext
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Oh great, another superhero movie. Just what the world needed. I loved sitting through 3 hours of CGI vomit.
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```
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**Output:**
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NEGATIVE (93.81% confidence)
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```
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### Example 3: Negative question
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**Input:**
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```plaintext
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Why did they even produce it?
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```
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**Output:**
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```plaintext
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NEGATIVE (99.37% confidence)
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```
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## Training code
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The full training code can be found in this repo as `train.ipynb`.
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```plaintext
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This movie was an absolute masterpiece! The acting was incredible and I loved every second.
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```
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**Output:** POSITIVE (99.03% confidence)
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### Example 2: Sarcasm
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**Input:**
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```plaintext
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Oh great, another superhero movie. Just what the world needed. I loved sitting through 3 hours of CGI vomit.
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```
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**Output:** NEGATIVE (93.81% confidence)
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### Example 3: Negative question
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**Input:**
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```plaintext
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Why did they even produce it?
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```
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**Output:** NEGATIVE (99.37% confidence)
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## Training code
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The full training code can be found in this repo as `train.ipynb`.
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