Add new model trained on sentiment140

README.md

@@ -212,8 +212,8 @@ The following pre-trained models are available for use:
 | Dataset | Vectorizer | Classifier | Features | Accuracy on test | Accuracy on self | Model |
 | --- | --- | --- | --- | --- | --- | --- |
 | `imdb50k` | `tfidf` | `LinearRegression` | 20 000 | 59.59% ± 0.73% | 74.08% ± 0.14% | [Here](models/imdb50k_tfidf_ft20000.pkl) |
-| `sentiment140` | `tfidf` | `LinearRegression` | 20 000 | ❌ | ❌ | [Here](models/sentiment140_tfidf_ft20000.pkl) |
-| `amazonreviews` | `hashing` | `LinearRegression` | 1 048 576 | ❌ | ❌ | [Here](models/amazonreviews_hashing_ft1048576.pkl) |
+| `sentiment140` | `tfidf` | `LinearRegression` | 20 000 | 52.81% ± 0.03% | ❌ | [Here](models/sentiment140_tfidf_ft20000.pkl) |
+| `amazonreviews` | `tfidf` | `LinearRegression` | 20 000 | ❌ | ❌ | [Here](models/amazonreviews_tfidf_ft1048576.pkl) |
 
 
 ## License

app/model.py

@@ -36,7 +36,7 @@ def _identity(x: list[str]) -> list[str]:
 def _get_vectorizer(
     name: Literal["tfidf", "count", "hashing"],
     n_features: int,
-    df: tuple[float, float] = (0.1, 0.9),
+    df: tuple[float, float] = (0.1, 1.0),
     ngram: tuple[int, int] = (1, 2),
 ) -> TransformerMixin:
     """Get the appropriate vectorizer.

models/sentiment140_tfidf_ft20000.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef80fcab97f510984f6f75f4579b817b9d2d361f68fc8dd24a3e3e9957fccfcf
+size 1230