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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+stage1/encoder/tokenizer.json filter=lfs diff=lfs merge=lfs -text

stage1/config.json

@@ -0,0 +1,22 @@
+{
+  "threshold": 0.3,
+  "threshold_note": "0.3 recommended for high Journal recall (100% on quality sample)",
+  "target_recall": 0.95,
+  "auc": 0.9843,
+  "encoder": "microsoft/harrier-oss-v1-0.6b",
+  "input_dim": 1024,
+  "hidden_dim": 512,
+  "metrics": {
+    "relevant_precision": 0.84,
+    "relevant_recall": 0.87,
+    "relevant_f1": 0.85,
+    "irrelevant_precision": 0.98,
+    "irrelevant_recall": 0.97,
+    "accuracy": 0.95
+  },
+  "quality_check": {
+    "sample_size": 96,
+    "accuracy_threshold_0.3": 0.969,
+    "journal_recall": 1.0
+  }
+}

stage1/encoder/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": true,
+    "include_prompt": true
+}

stage1/encoder/README.md

@@ -0,0 +1,238 @@
+---
+tags:
+- mteb
+- sentence-transformers
+- transformers
+language:
+- multilingual
+- af
+- am
+- ar
+- as
+- az
+- be
+- bg
+- bn
+- br
+- bs
+- ca
+- cs
+- cy
+- da
+- de
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- fy
+- ga
+- gd
+- gl
+- gu
+- ha
+- he
+- hi
+- hr
+- hu
+- hy
+- id
+- is
+- it
+- ja
+- jv
+- ka
+- kk
+- km
+- kn
+- ko
+- ku
+- ky
+- la
+- lo
+- lt
+- lv
+- mg
+- mk
+- ml
+- mn
+- mr
+- ms
+- my
+- ne
+- nl
+- 'no'
+- om
+- or
+- pa
+- pl
+- ps
+- pt
+- ro
+- ru
+- sa
+- sd
+- si
+- sk
+- sl
+- so
+- sq
+- sr
+- su
+- sv
+- sw
+- ta
+- te
+- th
+- tl
+- tr
+- ug
+- uk
+- ur
+- uz
+- vi
+- xh
+- yi
+- zh
+license: mit
+---
+
+## harrier-oss-v1
+
+harrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft.
+The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings.
+They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**.
+The models achieve state-of-the-art results on the [Multilingual MTEB v2](https://huggingface.co/spaces/mteb/leaderboard) benchmark as of the release date.
+
+| Model                                                                       | Parameters | Embedding Dimension | Max Tokens | MTEB v2 Score |
+|-----------------------------------------------------------------------------|------------|---------------------|------------|---------------|
+| [harrier-oss-v1-270m](https://huggingface.co/microsoft/harrier-oss-v1-270m) | 270M       | 640                 | 32,768     | 66.5          |
+| [harrier-oss-v1-0.6b](https://huggingface.co/microsoft/harrier-oss-v1-0.6b) | 0.6B       | 1,024               | 32,768     | 69.0          |
+| [harrier-oss-v1-27b](https://huggingface.co/microsoft/harrier-oss-v1-27b)   | 27B        | 5,376               | 32,768     | **74.3**      |
+
+## Training
+
+All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks.
+The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models.
+
+## Usage
+
+Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
+
+### Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("microsoft/harrier-oss-v1-0.6b", model_kwargs={"dtype": "auto"})
+
+queries = [
+    "how much protein should a female eat",
+    "summit define",
+]
+documents = [
+    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
+]
+
+query_embeddings = model.encode(queries, prompt_name="web_search_query")
+document_embeddings = model.encode(documents)
+
+scores = (query_embeddings @ document_embeddings.T) * 100
+print(scores.tolist())
+```
+
+Have a look at [config_sentence_transformers.json](config_sentence_transformers.json) for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `bitext_query`. You can also use a custom instruction directly via e.g. `model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: ")`.
+
+
+### Transformers
+
+```python
+import torch
+import torch.nn.functional as F
+
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+
+def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = last_hidden_states.shape[0]
+        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
+
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery: {query}'
+
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a web search query, retrieve relevant passages that answer the query'
+queries = [
+    get_detailed_instruct(task, 'how much protein should a female eat'),
+    get_detailed_instruct(task, 'summit define')
+]
+# No need to add instruction for retrieval documents
+documents = [
+    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
+]
+input_texts = queries + documents
+
+tokenizer = AutoTokenizer.from_pretrained('microsoft/harrier-oss-v1-0.6b')
+model = AutoModel.from_pretrained('microsoft/harrier-oss-v1-0.6b', dtype='auto')
+model.eval()
+model.cuda()
+
+max_length = 32768
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
+batch_dict = {k: v.cuda() for k, v in batch_dict.items()}
+
+outputs = model(**batch_dict)
+embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:2] @ embeddings[2:].T) * 100
+print(scores.tolist())
+```
+
+## Supported Languages
+
+The models are trained on multilingual data and support a wide range of languages,
+including but not limited to: Arabic, Bulgarian, Catalan, Czech, Danish, German, Greek, English, Spanish,
+Estonian, Persian, Finnish, French, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Italian, Japanese,
+Korean, Lithuanian, Latvian, Macedonian, Malay, Dutch, Norwegian, Polish, Portuguese, Romanian, Russian,
+Slovak, Slovenian, Albanian, Serbian, Swedish, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Chinese.
+
+## Evaluation
+
+Please follow the [mteb](https://github.com/embeddings-benchmark/mteb) repository on how to reproduce our scores.
+The evaluation prompts used for each task are also available at [mteb_v2_eval_prompts.json](mteb_v2_eval_prompts.json).
+
+## FAQ
+
+**1. Do I need to add instructions to the query?**
+
+Yes, this is how the model is trained, otherwise you will see a performance degradation.
+The task definition should be a one-sentence instruction that describes the task.
+This is a way to customize text embeddings for different scenarios through natural language instructions.
+
+On the other hand, there is no need to add instructions to the document side.
+
+**2. Why are my reproduced results slightly different from reported in the model card?**
+
+Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
+
+**3. What pooling strategy does this model use?**
+
+The model uses **last-token pooling** — the embedding of the last non-padding token is used as the sentence representation.
+The embedding is then L2-normalized. This is handled automatically when using Sentence Transformers.

stage1/encoder/added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

stage1/encoder/chat_template.jinja

@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

stage1/encoder/config.json

@@ -0,0 +1,60 @@
+{
+  "architectures": [
+    "Qwen3Model"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

stage1/encoder/config_sentence_transformers.json

@@ -0,0 +1,17 @@
+{
+  "prompts": {
+    "query": "",
+    "document": "",
+    "web_search_query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: ",
+    "sts_query": "Instruct: Retrieve semantically similar text\nQuery: ",
+    "bitext_query": "Instruct: Retrieve parallel sentences\nQuery: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "4.57.6",
+    "pytorch": "2.11.0+cu128"
+  }
+}

stage1/encoder/model.safetensors

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+oid sha256:ed3358eacbee7c6f35c97dda4f23cfc78bd62f20c4e44040f69d4e01c591e7ac
+size 2384233112

stage1/encoder/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

stage1/encoder/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 32768,
+    "do_lower_case": false
+}

stage1/encoder/special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

stage1/encoder/tokenizer.json

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+oid sha256:5f45684bb3bd50e1eb753e6bc438efc14329c293af236ecd331667b46657a3cc
+size 11423973

stage1/encoder/tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
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+      "special": false
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+  },
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+    "<|box_end|>",
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+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+  "max_length": 32768,
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+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": null
+}

stage1/neural_weights.pt

@@ -0,0 +1,3 @@
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