# Dataset Card for "Wizard-EvolInstruct70k-k64"

`centers.pt` in the files is a 64x384 matrix including the centers of each cluster. I use `sentence-transformers/all-MiniLM-L6-v2` to encode text.

```python
import torch
from sentence_transformers import SentenceTransformer

sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = torch.tensor(model.encode(sentences))
centers = torch.load("centers.pt")

# mse based cluster choice
clusters = (embeddings - centers).pow(2).mean(1).argmin().tolist()

# or you could load the sklearn kmeans classifier
# todo: documentation for that
# todo: figure out how to do that
# todo: cant you push sklearn classifiers to the hub with some weird code introduced earlier this year or something
```
region:us
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "cluster", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 131460545, "num_examples": 70000}], "download_size": 69258716, "dataset_size": 131460545}}
TAGS
#region-us 

# Dataset Card for "Wizard-EvolInstruct70k-k64"

'URL' in the files is a 64x384 matrix including the centers of each cluster. I use 'sentence-transformers/all-MiniLM-L6-v2' to encode text.


# Dataset Card for "Wizard-EvolInstruct70k-k64"

'URL' in the files is a 64x384 matrix including the centers of each cluster. I use 'sentence-transformers/all-MiniLM-L6-v2' to encode text.
TAGS
#region-us 
 # Dataset Card for "Wizard-EvolInstruct70k-k64"

'URL' in the files is a 64x384 matrix including the centers of each cluster. I use 'sentence-transformers/all-MiniLM-L6-v2' to encode text.