Add model

1_Pooling/config.json

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+{
+    "embedding_dimension": 384,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+base_model: NeuML/celeberty-small
+language: en
+license: apache-2.0
+---
+
+# CeleBERTy Small Embeddings
+
+This is a [CeleBERTy Small](https://hf.co/neuml/sportsbert-small) model fined-tuned using [sentence-transformers](https://www.SBERT.net). It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+The training dataset was generated using a random sample of [Wikipedia articles](https://huggingface.co/datasets/NeuML/wikipedia-celebrity-similarity) labeled as `celebrity`. 
+
+The model was trained by distilling embeddings from the larger [DenseOn](https://huggingface.co/lightonai/DenseOn) model using [EmbedDistillLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#embeddistillloss) over the generated training dataset.
+
+As noted in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), it's important that the base model is pretrained on a large corpus of relevant documents prior to distillation.
+
+## Usage (txtai)
+
+This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
+
+```python
+import txtai
+
+embeddings = txtai.Embeddings(path="neuml/celeberty-small-embeddings", content=True)
+embeddings.index(documents())
+
+# Run a query
+embeddings.search("query to run")
+```
+
+## Usage (Sentence-Transformers)
+
+Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer("neuml/celeberty-small-embeddings")
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (Hugging Face Transformers)
+
+The model can also be used directly with Transformers. 
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def meanpooling(output, mask):
+    embeddings = output[0] # First element of model_output contains all token embeddings
+    mask = mask.unsqueeze(-1).expand(embeddings.size()).float()
+    return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9)
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained("neuml/celeberty-small-embeddings")
+model = AutoModel.from_pretrained("neuml/celeberty-small-embeddings")
+
+# Tokenize sentences
+inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    output = model(**inputs)
+
+# Perform pooling. In this case, mean pooling.
+embeddings = meanpooling(output, inputs['attention_mask'])
+
+print("Sentence embeddings:")
+print(embeddings)
+```
+
+## Evaluation Results
+
+A [BEIR-compatible dataset](https://huggingface.co/datasets/NeuML/wikipedia-celebrity-similarity/tree/main/beir) was generated to facilitate the evaluation process. This is a separate random sample of Wikipedia articles alongside generated user queries. 
+
+Evaluation results are shown below. [NDCG](https://en.wikipedia.org/wiki/Discounted_cumulative_gain) is used as the evaluation metric.
+
+| Model                                                                               | Parameters | NDCG      | Index Time  | Search Time | Disk      |
+| ----------------------------------------------------------------------------------- | ---------- | --------- | ----------- | ----------- | --------- |
+| [**CeleBERTy Small Embeddings**](https://hf.co/neuml/celeberty-small-embeddings)    | **22.7M**  | **55.24** | **3.71s**    | **0.37s**   | **16 MB** |
+| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2)            | 22.7M      | 48.12     | 4.03s      | 0.41s       | 16 MB     |
+| [DenseOn](https://hf.co/lightonai/DenseOn)                                          | 149M       | 57.26     | 21.19s      | 0.76s       | 31 MB     |
+| [EmbeddingGemma](https://hf.co/google/embeddinggemma-300m)                          | 300M       | 58.61     | 27.37s      | 1.39s       | 31 MB     |
+| [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)            | 600M       | 54.02     | 34.02s     | 2.01s       | 41 MB     | 
+| [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B)                | 4000M      | 60.72     | 167.01s     | 9.34s       | 103 MB    |
+| [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B)                | 8000M      | 61.04     | 283.28s     | 16.05s      | 164 MB    |
+
+This model is a solid performer at a small size. It beats the same sized `all-MiniLM-L6-v2` model by a significant margin. It beats the 600M parameter Qwen3 Embeddings model which is over 25x larger. It scores slightly lower than the model it's distilled from (`DenseOn`). 
+
+This is a great model that can be used in CPU-only setups without trading off much on the accuracy front. It shows how small models can excel at specialized domains, requiring less compute and disk space. 
+
+## Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## More Information
+
+Read more about the model in [this article](https://huggingface.co/blog/NeuML/celeberty-small).

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.11.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.0",
+    "transformers": "5.11.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "document: ",
+    "query": "query: "
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c585beb23b400ab6e9c586d10a2bc6ad26d64f3ad20b33671e49cc417fd04e8
+size 90864192

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": true,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}