diff --git a/req2.txt b/req2.txt
deleted file mode 100644
index 9bc6c5ef8e961964364a6eb3da3b4a9ec6dc1ec3..0000000000000000000000000000000000000000
--- a/req2.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-dvc-gdrive==2.19.0
-dvclive==2.12.0
-pandas==1.4.2
-pip-chill==1.0.3
-pydeprecate==0.3.2
-pytorch-lightning==2.0.4
-pytorch-triton-rocm==2.0.2
-sentence-transformers==2.2.0
-#tensorflow==2.8.0
-wandb==0.12.11
diff --git a/requirements.txt b/requirements.txt
index bc59ed160620d8fd3ea200a5133defe832d6138b..c11de2f6da1fd5284c1fce2662a71ff6e5f8a40e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,6 +7,7 @@ shap==0.41.0
tqdm>=4.65.0
tokenizers==0.13.2
sentence-transformers==2.2.2
+pandas==1.4.2
jupyter
matplotlib
spacy
@@ -15,6 +16,8 @@ https://github.com/explosion/spacy-models/releases/download/pl_core_news_lg-3.5.
--find-links https://download.pytorch.org/whl/torch_stable.html
torch==1.12.0+cu116
+pytorch-lightning==2.0.4
+wandb==0.12.11
--index-url https://pypi.clarin-pl.eu/simple/
plwn-api==0.24
diff --git a/storage/data/.gitignore b/storage/data/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e9800040b3eb6fb42a6bbb093e1e7cfe72e8f06e
--- /dev/null
+++ b/storage/data/.gitignore
@@ -0,0 +1 @@
+/unhealthy_conversations
diff --git a/storage/data/unhealthy_conversations.dvc b/storage/data/unhealthy_conversations.dvc
new file mode 100644
index 0000000000000000000000000000000000000000..cfbee93809ca49ff0d12e6eb857510ce2b0cb90c
--- /dev/null
+++ b/storage/data/unhealthy_conversations.dvc
@@ -0,0 +1,5 @@
+outs:
+- md5: 9c3abc0871f83db29875edb972144e54.dir
+ size: 235178486
+ nfiles: 7
+ path: unhealthy_conversations
diff --git a/storage/trained_models/.gitignore b/storage/trained_models/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e9800040b3eb6fb42a6bbb093e1e7cfe72e8f06e
--- /dev/null
+++ b/storage/trained_models/.gitignore
@@ -0,0 +1 @@
+/unhealthy_conversations
diff --git a/storage/trained_models/unhealthy_conversations.dvc b/storage/trained_models/unhealthy_conversations.dvc
new file mode 100644
index 0000000000000000000000000000000000000000..97784aceb67d2d5c9fa39d4df9b1319f7fa16cbd
--- /dev/null
+++ b/storage/trained_models/unhealthy_conversations.dvc
@@ -0,0 +1,5 @@
+outs:
+- md5: 5473df6f986edca362433e1de30e98cf.dir
+ size: 4978942
+ nfiles: 42
+ path: unhealthy_conversations
diff --git a/text_attacks/models/unhealthy.py b/text_attacks/models/unhealthy.py
new file mode 100644
index 0000000000000000000000000000000000000000..b341eb86ce8152cfd28c87389402c3b3d1a737a9
--- /dev/null
+++ b/text_attacks/models/unhealthy.py
@@ -0,0 +1,109 @@
+"""Classification model for ag_news"""
+import torch
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+from personalized_nlp.learning.classifier import Classifier
+from personalized_nlp.models import models as models_dict
+
+
+datamodule = UnhealthyDataModule()
+output_dim = (
+ sum(datamodule.class_dims)
+)
+text_embedding_dim = datamodule.text_embedding_dim
+model_cls = models_dict["baseline"]
+
+
+
+def get_model_and_tokenizer():
+ tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE")
+ model = model_cls(
+ output_dim=output_dim,
+ text_embedding_dim=text_embedding_dim,
+ annotator_num=datamodule.annotators_number,
+ bias_vector_length=len(datamodule.class_dims),
+ )
+ model = Classifier(
+ model=model, lr=1e-2, class_dims=class_dims, class_names=class_names
+ )
+ model.load_state_dict(torch.load(
+ "trained_models/unhealthy/baseline.pt")["state_dict"]
+ )
+ model.eval()
+ return model, tokenizer
+
+
+
+def get_embeddings(texts, tokenizer, model, max_seq_len=256, mean_pooling=True):
+ model = model.eval()
+
+ texts = [str(t) for t in texts]
+ def batch(iterable, n=1):
+ l = len(iterable)
+ for ndx in range(0, l, n):
+ yield iterable[ndx : min(ndx + n, l)]
+ def fun(texts):
+ logits = list()
+ i = 0
+ for chunk in tqdm(
+ [texts[pos:pos + 128] for pos in range(0, len(texts), 128)]
+ ):
+ encoded_inputs = tokenizer(
+ chunk,
+ return_tensors="pt",
+ padding=True,
+ truncation=True,
+ max_length=512
+ ).to(device)
+ with torch.no_grad():
+ logits.append(model(**encoded_inputs).logits.cpu())
+ logits = torch.cat(logits, dim=0)
+ pred_y = torch.argmax(logits, dim=1).tolist()
+ pred_y = [model.config.id2label[p] for p in pred_y]
+ return pred_y
+
+ return fun
+
+ device = "cuda"
+
+ all_embeddings = []
+ for batched_texts in batch(texts, 200):
+ with torch.no_grad():
+ batch_encoding = tokenizer.batch_encode_plus(
+ batched_texts,
+ padding="longest",
+ add_special_tokens=True,
+ truncation=True,
+ max_length=max_seq_len,
+ return_tensors="pt"
+ ).to(device)
+ emb = model(**batch_encoding)
+
+ if mean_pooling:
+ mask = batch_encoding["attention_mask"] > 0
+
+ for i in range(emb[0].size()[0]):
+ emb_pooled = emb[0][i, mask[i] > 0, :].mean(axis=0)[None, :]
+ all_embeddings.append(emb_pooled)
+ else:
+ emb_pooled = emb.last_hidden_state[:, 0, :]
+ all_embeddings.append(emb_pooled)
+
+ return {"embeddings": torch.cat(all_embeddings, axis=0).to("cpu").cpu()}
+
+
+def get_classify_funcion(device="cpu"):
+
+ model, tokenizer = get_model_and_tokenizer()
+ model.eval()
+ model = model.to(device)
+ language_model = AutoModel.from_pretrained(
+ "sentence-transformers/LaBSE"
+ ).to("cuda")
+
+ def predict(texts):
+ embeddings = get_embeddings(texts, tokenizer, language_model)
+ return model(embeddings)
+
+ return predict