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Improved models
Browse files- README.md +2 -2
- app/model.py +0 -6
- models/imdb50k_tfidf_ft20000.pkl +2 -2
- models/sentiment140_tfidf_ft20000.pkl +2 -2
README.md
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The following pre-trained models are available for use:
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| Dataset | Vectorizer | Classifier | Features | Accuracy on test | Accuracy on self | Model |
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| --- | --- | --- | --- | --- | --- | --- |
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| `imdb50k` | `tfidf` | `LinearRegression` | 20 000 |
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| `sentiment140` | `tfidf` | `LinearRegression` | 20 000 |
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| `amazonreviews` | `tfidf` | `LinearRegression` | 20 000 | ❌ | ❌ | [Here](models/amazonreviews_tfidf_ft1048576.pkl) |
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The following pre-trained models are available for use:
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| Dataset | Vectorizer | Classifier | Features | Accuracy on test | Accuracy on self | Model |
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| --- | --- | --- | --- | --- | --- | --- |
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+
| `imdb50k` | `tfidf` | `LinearRegression` | 20 000 | 83.24% ± 0.99% | 89.24% ± 0.13% | [Here](models/imdb50k_tfidf_ft20000.pkl) |
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| `sentiment140` | `tfidf` | `LinearRegression` | 20 000 | 83.24% ± 0.99% | 77.32% ± 0.28% | [Here](models/sentiment140_tfidf_ft20000.pkl) |
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| `amazonreviews` | `tfidf` | `LinearRegression` | 20 000 | ❌ | ❌ | [Here](models/amazonreviews_tfidf_ft1048576.pkl) |
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app/model.py
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@@ -36,7 +36,6 @@ def _identity(x: list[str]) -> list[str]:
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def _get_vectorizer(
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name: Literal["tfidf", "count", "hashing"],
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n_features: int,
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df: tuple[float, float] = (1.0, 1.0),
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ngram: tuple[int, int] = (1, 2),
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) -> TransformerMixin:
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"""Get the appropriate vectorizer.
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Args:
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name: Type of vectorizer
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n_features: Maximum number of features
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df: Document frequency range [min_df, max_df] (ignored for HashingVectorizer)
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ngram: N-gram range [min_n, max_n]
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Returns:
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case "tfidf":
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return TfidfVectorizer(
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max_features=n_features,
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min_df=df[0],
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max_df=df[1],
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**shared_params,
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)
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case "count":
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return CountVectorizer(
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max_features=n_features,
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min_df=df[0],
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max_df=df[1],
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**shared_params,
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)
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case "hashing":
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def _get_vectorizer(
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name: Literal["tfidf", "count", "hashing"],
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n_features: int,
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ngram: tuple[int, int] = (1, 2),
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) -> TransformerMixin:
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"""Get the appropriate vectorizer.
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Args:
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name: Type of vectorizer
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n_features: Maximum number of features
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ngram: N-gram range [min_n, max_n]
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Returns:
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case "tfidf":
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return TfidfVectorizer(
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max_features=n_features,
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**shared_params,
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)
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case "count":
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return CountVectorizer(
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max_features=n_features,
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**shared_params,
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)
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case "hashing":
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models/imdb50k_tfidf_ft20000.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c916d380fc84a33f3cb5892cd10e4aaa29330cbbac4243860e91fe9392df897
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size 398706
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models/sentiment140_tfidf_ft20000.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:1308cb96bbee2befeb585c99fb3ad78b4bbef0504fcb5070d8c738289c212431
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size 397501
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