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README.md

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+---
+license: other
+license_name: som-1.0
+license_link: https://github.com/jxmorris12/vec2text/blob/master/LICENSE
+library_name: vec2text
+tags:
+  - vec2text
+  - embedding-inversion
+  - clip
+  - text-embedding-inversion
+language:
+  - en
+---
+
+# clip-text-msmarco-inversion
+
+A **vec2text inversion (hypothesizer) model** that reconstructs text from the
+embeddings produced by the **CLIP ViT-L/14 text encoder** (the text encoder used by
+Stable Diffusion v1.5, identical to [`openai/clip-vit-large-patch14`](https://huggingface.co/openai/clip-vit-large-patch14)).
+
+It is the first-stage model: given a CLIP text embedding, it produces an initial
+text hypothesis. Pair it with the corrector model
+[`Afrostnova/clip-text-msmarco-corrector`](https://huggingface.co/Afrostnova/clip-text-msmarco-corrector)
+to iteratively refine that hypothesis.
+
+- **Base architecture:** `vec2text` `InversionModel` (T5-based encoder–decoder)
+- **Embedder:** `CLIPTextModel` — `openai/clip-vit-large-patch14`
+- **Training data:** MS MARCO
+- **Embedding transform:** `repeat` (`num_repeat_tokens=16`)
+
+## ⚠️ Requirements
+
+This is **not** loadable with the upstream `pip install vec2text` package — upstream
+does not support a `CLIPTextModel` embedder. You need the fork that adds CLIP support
+(the one this model was trained with). With the wrong version, `from_pretrained` fails
+because the `CLIPTextModel` embedder is unknown.
+
+## Usage
+
+```python
+import vec2text
+
+inv = vec2text.models.InversionModel.from_pretrained("Afrostnova/clip-text-msmarco-inversion")
+cor = vec2text.models.CorrectorEncoderModel.from_pretrained("Afrostnova/clip-text-msmarco-corrector")
+corrector = vec2text.load_corrector(inv, cor)
+
+# `embeddings` = CLIP text-encoder last_hidden_state, pooled at the EOS position.
+text = vec2text.invert_embeddings(
+    embeddings=embeddings,   # (batch, hidden_dim) on the same device as the model
+    corrector=corrector,
+    num_steps=20,
+    sequence_beam_width=1,
+)
+```
+
+The CLIP text encoder is loaded automatically when the inversion model is instantiated.
+On a clean machine it is fetched from `openai/clip-vit-large-patch14`; override with the
+`CLIP_TEXT_ENCODER` environment variable if you want a local copy.