Push model using huggingface_hub.

README.md

@@ -1,22 +1,8 @@
-# Assuming training and testing data are using the same names as we did in the skeleton code provided by the TA
-
-X_train = dataset_train['title']
-y_train = dataset_train['labels']
-
-X_test = dataset_test['title']
-y_test = dataset_test['labels']
-
-from sklearn.feature_extraction.text import TfidfVectorizer
-tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2), stop_words='english')
-X_train_tfidf = tfidf.fit_transform(X_train)
-X_test_tfidf = tfidf.transform(X_test)
-
-
-from sklearn.svm import SVC
-svm_model = SVC(kernel='linear', random_state=42)
-svm_model.fit(X_train_tfidf, y_train)
-y_pred = svm_model.predict(X_test_tfidf)
-accuracy = accuracy_score(y_test, y_pred)
-print(f"Random Forest Accuracy: {accuracy:.4f}")
-print(classification_report(y_test, y_pred))
-
+---
+tags:
+- model_hub_mixin
+---
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Library: [More Information Needed]
+- Docs: [More Information Needed]

config.json

@@ -0,0 +1,4 @@
+{
+  "model_name": "SVM",
+  "num_labels": 2
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0daefdf1f01cdd491ddb99b0fabfad05df1fbc1fc7f4e6513a8ad83c3a2eb9e8
+size 349