typos
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@ -47,8 +47,8 @@ class GeneralizedFunnelling:
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self.posteriors_vgf = posterior
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self.wce_vgf = wce
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self.multilingual_vgf = multilingual
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self.textual_trasformer_vgf = textual_transformer
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self.visual_transformer_vgf = visual_transformer
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self.textual_trf_vgf = textual_transformer
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self.visual_trf_vgf = visual_transformer
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self.probabilistic = probabilistic
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self.num_labels = num_labels
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# ------------------------
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@ -56,7 +56,7 @@ class GeneralizedFunnelling:
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self.embed_dir = embed_dir
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self.cached = True
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# Textual Transformer VGF params ----------
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self.textaul_transformer_name = textual_transformer_name
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self.textual_trf_name = textual_transformer_name
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self.epochs = epochs
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self.lr_transformer = lr
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self.batch_size_transformer = batch_size
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@ -66,7 +66,7 @@ class GeneralizedFunnelling:
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self.evaluate_step = evaluate_step
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self.device = device
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# Visual Transformer VGF params ----------
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self.visual_transformer_name = visual_transformer_name
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self.visual_trf_name = visual_transformer_name
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# Metaclassifier params ------------
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self.optimc = optimc
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# -------------------
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@ -142,10 +142,10 @@ class GeneralizedFunnelling:
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wce_vgf = WceGen(n_jobs=self.n_jobs)
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self.first_tier_learners.append(wce_vgf)
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if self.textual_trasformer_vgf:
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if self.textual_trf_vgf:
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transformer_vgf = TextualTransformerGen(
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dataset_name=self.dataset_name,
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model_name=self.textaul_transformer_name,
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model_name=self.textual_trf_name,
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lr=self.lr_transformer,
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epochs=self.epochs,
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batch_size=self.batch_size_transformer,
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@ -159,7 +159,7 @@ class GeneralizedFunnelling:
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)
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self.first_tier_learners.append(transformer_vgf)
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if self.visual_transformer_vgf:
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if self.visual_trf_vgf:
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visual_trasformer_vgf = VisualTransformerGen(
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dataset_name=self.dataset_name,
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model_name="vit",
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@ -198,8 +198,8 @@ class GeneralizedFunnelling:
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self.posteriors_vgf,
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self.multilingual_vgf,
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self.wce_vgf,
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self.textual_trasformer_vgf,
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self.visual_transformer_vgf,
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self.textual_trf_vgf,
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self.visual_trf_vgf,
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self.aggfunc,
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)
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print(f"- model id: {self._model_id}")
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@ -373,7 +373,7 @@ class GeneralizedFunnelling:
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"rb",
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) as vgf:
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first_tier_learners.append(pickle.load(vgf))
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if self.textual_trasformer_vgf:
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if self.textual_trf_vgf:
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with open(
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os.path.join(
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"models", "vgfs", "transformer", f"transformerGen_{model_id}.pkl"
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