Upload PhraseBERT ONNX model

README.md

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+---
+language: en
+license: apache-2.0
+library_name: onnxruntime
+tags:
+  - onnx
+  - phrase-embeddings
+  - sentence-similarity
+  - feature-extraction
+base_model: whaleloops/phrase-bert
+pipeline_tag: feature-extraction
+---
+
+# PhraseBERT ONNX
+
+ONNX export of [whaleloops/phrase-bert](https://huggingface.co/whaleloops/phrase-bert) for lightweight inference using ONNX Runtime — **no PyTorch or Transformers required**.
+
+## Model Details
+
+- **Base model**: whaleloops/phrase-bert (BERT-base, 12 layers, 768 hidden dim)
+- **Pooling**: Mean pooling (attention-mask weighted)
+- **Format**: ONNX
+- **Size**: ~416 MB
+
+## Usage
+
+### Install dependencies (no torch/transformers needed)
+
+```bash
+pip install onnxruntime tokenizers numpy
+```
+
+### Download and run
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download the model
+model_dir = snapshot_download("langminer/phrase-bert-onnx")
+```
+
+```python
+import numpy as np
+import onnxruntime as ort
+from tokenizers import Tokenizer
+
+# Load model and tokenizer
+session = ort.InferenceSession(f"{model_dir}/model.onnx", providers=["CPUExecutionProvider"])
+tokenizer = Tokenizer.from_file(f"{model_dir}/tokenizer.json")
+tokenizer.enable_padding(pad_id=0, pad_token="[PAD]")
+tokenizer.enable_truncation(max_length=512)
+
+# Encode phrases
+phrases = ["play an active role", "participate actively", "machine learning"]
+encodings = tokenizer.encode_batch(phrases)
+
+input_ids = np.array([e.ids for e in encodings], dtype=np.int64)
+attention_mask = np.array([e.attention_mask for e in encodings], dtype=np.int64)
+token_type_ids = np.array([e.type_ids for e in encodings], dtype=np.int64)
+
+# Run inference
+outputs = session.run(None, {
+    "input_ids": input_ids,
+    "attention_mask": attention_mask,
+    "token_type_ids": token_type_ids,
+})
+token_embeddings = outputs[0]  # (batch, seq_len, 768)
+
+# Mean pooling
+mask = attention_mask[:, :, np.newaxis].astype(np.float32)
+embeddings = np.sum(token_embeddings * mask, axis=1) / np.sum(mask, axis=1)
+
+print(embeddings.shape)  # (3, 768)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{wang2021phrase,
+  title={Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration},
+  author={Wang, Shufan and Thompson, Laure and Iyyer, Mohit},
+  booktitle={EMNLP},
+  year={2021}
+}
+```

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "export_model_type": "transformer",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.6",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4635d62a5fd44f49076b287449aa8ad0a9af6b66306e9ce7edf6098ba2c95f9b
+size 435812071

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}