Duplicate from onnx-community/gte-multilingual-base

Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>

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README.md

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+---
+base_model: Alibaba-NLP/gte-multilingual-base
+library_name: transformers.js
+---
+
+https://huggingface.co/Alibaba-NLP/gte-multilingual-base with ONNX weights to be compatible with Transformers.js.
+
+## Usage (Transformers.js)
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+You can then use the model to compute embeddings, as follows:
+
+```js
+import { pipeline } from '@huggingface/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'onnx-community/gte-multilingual-base');
+
+// Compute sentence embeddings
+const texts = ['Hello world.', 'Example sentence.'];
+const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
+console.log(embeddings);
+// Tensor {
+//   dims: [ 2, 768 ],
+//   type: 'float32',
+//   data: Float32Array(1536) [ 0.019079938530921936, 0.041718777269124985, ... ],
+//   size: 1536
+// }
+
+console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list
+// [
+//   [ -0.04247443005442619, 0.00007914059096947312, -0.007467088755220175, ... ],
+//   [ -0.05675575137138367, 0.0288529209792614, -0.02864679880440235, ... ]
+// ]
+```
+
+You can also use the model for retrieval. For example:
+```js
+import { pipeline, cos_sim } from '@huggingface/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'onnx-community/gte-multilingual-base');
+
+// List of documents you want to embed
+const texts = [
+    'Hello world.',
+    'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.',
+    'I love pandas so much!',
+];
+
+// Compute sentence embeddings
+const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
+
+// Prepend recommended query instruction for retrieval.
+const query_prefix = 'Represent this sentence for searching relevant passages: '
+const query = query_prefix + 'What is a panda?';
+const query_embeddings = await extractor(query, { pooling: 'mean', normalize: true });
+
+// Sort by cosine similarity score
+const scores = embeddings.tolist().map(
+    (embedding, i) => ({
+        id: i,
+        score: cos_sim(query_embeddings.data, embedding),
+        text: texts[i],
+    })
+).sort((a, b) => b.score - a.score);
+console.log(scores);
+// [
+//   { id: 1, score: 0.8908273895482127, text: 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.' },
+//   { id: 2, score: 0.7903781165100383, text: 'I love pandas so much!' },
+//   { id: 0, score: 0.7320514921911025, text: 'Hello world.' }
+// ]
+```
+
+---
+
+Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

config.json

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+{
+  "_name_or_path": "Alibaba-NLP/gte-multilingual-base",
+  "architectures": [
+    "NewModel",
+    "NewForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "export_model_type": "transformer",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 8.0,
+    "type": "ntk"
+  },
+  "rope_theta": 20000,
+  "torch_dtype": "float16",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

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quantize_config.json

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+{
+    "modes": [
+        "fp16",
+        "q8",
+        "int8",
+        "uint8",
+        "q4",
+        "q4f16",
+        "bnb4"
+    ],
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+    "reduce_range": true,
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+    "is_symmetric": true,
+    "accuracy_level": null,
+    "quant_type": 1
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "single_word": false
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+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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tokenizer_config.json

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+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 32768,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}