Update README.md

README.md

@@ -16,6 +16,78 @@ dataset_info:
   download_size: 112131877
   dataset_size: 247037602
 ---
+
 # Dataset Card for "roots-viz-data"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+```python
+import os
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfTransformer
+from sklearn.decomposition import TruncatedSVD
+from tqdm.notebook import tqdm
+from openTSNE import TSNE
+import datashader as ds
+import colorcet as cc
+
+import vectorizers
+from vectorizers.transformers import CountFeatureCompressionTransformer, InformationWeightTransformer
+
+from dask.distributed import Client, LocalCluster
+import dask.dataframe as dd
+import dask_ml.feature_extraction.text
+import dask.bag as db
+
+from transformers import AutoTokenizer, AutoModel
+from huggingface_hub import notebook_login, HfApi, hf_hub_download, Repository
+from datasets import load_dataset
+from datasets.utils.py_utils import convert_file_size_to_int
+
+def batch_tokenize(batch):
+    return {'tokenized': [' '.join(e.tokens) for e in tokenizer(batch['text']).encodings]}
+
+dset = dset.map(batch_tokenize, batched=True, batch_size=64, num_proc=28)
+
+max_shard_size = convert_file_size_to_int('300MB')
+dataset_nbytes = dset.data.nbytes
+num_shards = int(dataset_nbytes / max_shard_size) + 1
+num_shards = max(num_shards, 1)
+print(f"Sharding into {num_shards} files.")
+os.makedirs(f"{dset_name}/tokenized", exist_ok=True)
+for shard_index in tqdm(range(num_shards)):
+    shard = dset.shard(num_shards=num_shards, index=shard_index, contiguous=True)
+    shard.to_parquet(f"{dset_name}/tokenized/tokenized-{shard_index:03d}.parquet")
+
+client = Client()
+client
+
+df = dd.read_parquet(f'{dset_name}/tokenized/')
+vect = dask_ml.feature_extraction.text.CountVectorizer(tokenizer=str.split,
+                                                       token_pattern=None,
+                                                       vocabulary=vocab)
+tokenized_bag = df['tokenized'].to_bag()
+X = vect.transform(tokenized_bag)
+
+counts = X.compute()
+client.shutdown()
+
+tfidf_transformer = TfidfTransformer(sublinear_tf=True, norm="l2")
+tfidf = tfidf_transformer.fit_transform(counts)
+
+svd = TruncatedSVD(n_components=160)
+X_svd = svd.fit_transform(tfidf)
+
+tsne = TSNE(
+    perplexity=30,
+    n_jobs=28,
+    random_state=42,
+    verbose=True,
+)
+
+tsne_embedding = tsne.fit(X)
+
+df = pd.DataFrame(data=tsne_embedding, columns=['x','y'])
+agg = ds.Canvas(plot_height=600, plot_width=600).points(df, 'x', 'y')
+img = ds.tf.shade(agg, cmap=cc.fire, how='eq_hist')
+ds.tf.set_background(img, "black")
+```