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README.md

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+---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: text
+    dtype: string
+  - name: embedding
+    list: float32
+    length: 768
+  splits:
+  - name: train
+
+    num_examples: 629346
+
+  dataset_size: 629346
+  download_size: 629346
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train/*
+metadata:
+  model_name: Alibaba-NLP/gte-modernbert-base
+  dataset_name: stanfordnlp/snli
+---
+
+# Embedpress: Alibaba-NLP/gte-modernbert-base on the stanfordnlp/snli dataset
+
+This is the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset,
+embedded with [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base).
+
+For each example, we embed the text directly (no additional instruction prompt).
+Embeddings have dimensionality **768**.
+
+These embeddings are intended for tasks like large-scale distillation, retrieval, and similarity search.
+Because the raw text may exceed the model’s limit, we recommend truncating to the model’s maximum token length at build time.
+
+## Schema
+
+- `id` *(string)* — unique identifier
+- `text` *(string)* — the query text used for embedding
+- `embedding` *(float32[768])* — the vector representation from `Alibaba-NLP/gte-modernbert-base`
+
+## Split
+
+- `train` — **629346 examples**
+
+## Notes
+
+- Produced with `Alibaba-NLP/gte-modernbert-base` from Hugging Face Hub.
+- If you need a smaller embedding size (e.g., matryoshka/truncated vectors), you can safely slice the embeddings without re-embedding.

metadata.json

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+{
+  "model_name": "Alibaba-NLP/gte-modernbert-base",
+  "dataset_name": "stanfordnlp/snli"
+}

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