Add model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+documents filter=lfs diff=lfs merge=lfs -text
+embeddings filter=lfs diff=lfs merge=lfs -text
+graph filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+inference: false
+language: en
+license: apache-2.0
+library_name: txtai
+tags:
+- sentence-similarity
+datasets:
+- maxiw/hf-posts
+---
+
+# Hugging Face Posts txtai embeddings index
+
+This is a [txtai](https://github.com/neuml/txtai) embeddings index for the [Hugging Face Posts dataset](https://huggingface.co/datasets/maxiw/hf-posts).
+
+txtai must be [installed](https://neuml.github.io/txtai/install/) to use this model.
+
+## Example
+
+This index can be loaded from the Hugging Face Hub with txtai as shown below.
+
+```python
+from txtai import Embeddings
+
+# Load the index from the HF Hub
+embeddings = Embeddings()
+embeddings.load(provider="huggingface-hub", container="neuml/txtai-hfposts")
+
+# Search for posts discussing transformers
+embeddings.search("transformers")
+```
+
+## Use Cases
+
+Hugging Face Posts is an exploratory dataset to analyze what is being discussed on the [Hugging Face Posts](https://huggingface.co/posts) platform.
+
+An embeddings index generated by txtai is a fully encapsulated index format. It doesn't require a database server or dependencies outside of the Python install.
+
+## More information
+
+Read more about this model and how it was built in [this article]().

config.json

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+{
+  "autoid": "uuid5",
+  "path": "intfloat/e5-large",
+  "instructions": {
+    "query": "query: ",
+    "data": "passage: "
+  },
+  "content": true,
+  "graph": {
+    "approximate": false,
+    "minscore": 0.7,
+    "backend": "networkx"
+  },
+  "dimensions": 1024,
+  "backend": "faiss",
+  "offset": 2454,
+  "build": {
+    "create": "2024-11-21T16:05:41Z",
+    "python": "3.9.20",
+    "settings": {
+      "components": "IDMap,Flat"
+    },
+    "system": "Linux (x86_64)",
+    "txtai": "8.1.0"
+  },
+  "update": "2024-11-21T16:05:41Z"
+}

documents

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+oid sha256:42e4b5c483a9d53376a2f3fb4923181cf3f5af8c8c58f688605768ede1a15dc6
+size 5894144

embeddings

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graph

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