task_categories:sentence-similarity size_categories:100K<n<1M language:en region:us
---
task_categories:
- sentence-similarity
language:
- en
size_categories:
- 100K<n<1M
---

A dataset that contains all data except those labeled as 'neutral' in 'https://sbert.net/datasets/AllNLI.tsv.gz'' which the corresponding text embedding produced by 'princeton-nlp/unsup-simcse-roberta-large'. The features are transformed to a size of 256 by the PCA object.

In order to load the dictionary of the teacher embeddings corresponding to the anli dataset:

```python
!git clone https://huggingface.co/datasets/zen-E/ANLI-simcse-roberta-large-embeddings-pca-256

# if dimension reduction to 256 is required
import joblib
pca = joblib.load('ANLI-simcse-roberta-large-embeddings-pca-256/pca_model.sav')

teacher_embeddings = torch.load("./ANLI-simcse-roberta-large-embeddings-pca-256/anli_train_simcse_robertra_sent_embed.pt")
if pca is not None:
  all_sents = sorted(teacher_embeddings.keys())
  teacher_embeddings_values = torch.stack([teacher_embeddings[s] for s in all_sents], dim=0).numpy()
  teacher_embeddings_values_trans = pca.transform(teacher_embeddings_values)
  teacher_embeddings = {k:torch.tensor(v) for k, v in zip(all_sents, teacher_embeddings_values_trans)}
```