Create app.py

app.py

@@ -0,0 +1,54 @@
+import numpy as np
+from nomic import atlas
+import glob
+from tqdm import tqdm
+from datasets import load_dataset, concatenate_datasets
+from sklearn.decomposition import PCA
+
+files = glob.glob("inference/*.jsonl")
+print(files)
+df = concatenate_datasets([load_dataset("json", data_files=file, split="train") for file in tqdm(files)])
+
+print(len(df))
+print(df)
+
+df = df.map(lambda example: {"inputs": [prompt + "\n" + response for prompt, response in zip(example["prompt"], example["response"])]},
+            batched=True,
+            num_proc=64)
+
+df = df.map(lambda example: {"trained_on": [int(t) for t in example["is_train"]]},
+                batched=True,
+                num_proc=64)
+                
+df = df.remove_columns("is_train")
+
+text = df.remove_columns(["labels", "input_ids", "embeddings"])
+
+text_df = [text[i] for i in range(len(text))]
+
+atlas.map_text(text_df, indexed_field="inputs",
+               name="CHANGE ME!",
+               colorable_fields=["source", "loss", "trained_on"],
+               reset_project_if_exists=True,
+               )
+
+# index is local to train/test split, regenerate
+data = df.remove_columns(["labels", "input_ids", "index"])
+data = data.add_column("index", list(range(len(data))))
+# max embed dim is 2048 for now
+# note! this is slow in pyarrow/hf datasets
+embeddings = np.array(data["embeddings"])
+print("embeddings shape:", embeddings.shape)
+embeddings = PCA(n_components=2048).fit_transform(embeddings)
+
+data = data.remove_columns(["embeddings"])
+columns = data.to_pandas().to_dict("records") 
+
+atlas.map_embeddings(embeddings,
+                     data=columns,
+                     id_field="index",
+                     name="CHANGE ME!",
+                     colorable_fields=["source", "loss", "trained_on"],
+                     build_topic_model=True,
+                     topic_label_field="inputs",
+                     reset_project_if_exists=True,)