added all methods for text

.gitattributes

@@ -39,3 +39,7 @@ checkpoints/mixmatch.index filter=lfs diff=lfs merge=lfs -text
 checkpoints/fixmatch_tune.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
 checkpoints/fixmatch_tune.index filter=lfs diff=lfs merge=lfs -text
 checkpoints/freematch_tune.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+checkpoints/label_prop.index filter=lfs diff=lfs merge=lfs -text
+checkpoints/contrastive.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+checkpoints/contrastive.index filter=lfs diff=lfs merge=lfs -text
+checkpoints/label_prop.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -50,6 +50,16 @@ def ssl_predict(in_text, model_type):
         model.cls_head.load_weights("./checkpoints/mixmatch")
         preds = model([toks["input_ids"],toks["attention_mask"]], training=False)
 
+    elif model_type == "contrastive_reg":
+        model = FixMatchTune(encoder_name="readerbench/RoBERT-base")
+        model.cls_head.load_weights("./checkpoints/contrastive")
+        preds, _ = model([toks["input_ids"],toks["attention_mask"]], training=False)
+
+    elif model_type == "label_propagation":
+        model = LPModel()
+        model.cls_head.load_weights("./checkpoints/label_prop")
+        preds = model([toks["input_ids"],toks["attention_mask"]], training=False)
+
     probs = list(preds[0].numpy())
 
     d = {}

checkpoints/contrastive.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1cbd1032502d2c8ec25b9c2ec989223d36027381c66ddf32746f3772af03ec8
+size 461147140

checkpoints/contrastive.index

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:638d167c9d26ba37d1ba2d76ae7863e0edbc3c7b98213f53b9b310613c6e8598
+size 14764

checkpoints/label_prop.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33c1a8e6cada47450ebf64d466cbd5de26ba15c9d2bfec0a0ed607eaad0d92c1
+size 461147088

checkpoints/label_prop.index

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7847020dcdc7c07eee2ca947869fa9763f07e9aec75bf07574c0c9dc167ca9e
+size 14764

models.py

@@ -61,4 +61,25 @@ class MixMatch(tf.keras.Model):
         embeds = self.bert(input_ids=ids, attention_mask=mask,training=training).pooler_output
         augs = self.augment(embeds,training=training)
 
-        return self.cls_head(augs,training=training)
+        return self.cls_head(augs,training=training)
+    
+class LPModel(tf.keras.Model):
+    """label propagation"""
+    def __init__(self,bert_model="readerbench/RoBERT-base",num_classes=4,**kwargs):
+        super(LPModel,self).__init__(**kwargs)
+        self.bert = TFAutoModel.from_pretrained(bert_model)
+        self.num_classes = num_classes
+
+        self.cls_head = tf.keras.Sequential([
+            tf.keras.layers.Dense(256,activation="relu"),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.Dense(64,activation="relu"),
+            tf.keras.layers.Dense(self.num_classes, activation="softmax")
+        ])
+
+    def call(self, inputs, training):
+        ids, mask = inputs
+
+        embeds = self.bert(input_ids=ids, attention_mask=mask,training=training).pooler_output
+
+        return self.cls_head(embeds, training=training)