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bansalaman18
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reranker-msmarco-v1.1-ettin-encoder-150m-listnet

Text Ranking
sentence-transformers
Safetensors
English
modernbert
cross-encoder
reranker
Generated from Trainer
dataset_size:78704
loss:ListNetLoss
text-embeddings-inference
Model card Files
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Add new CrossEncoder model

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  1. README.md +615 -0
  2. config.json +57 -0
  3. model.safetensors +3 -0
  4. special_tokens_map.json +37 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +945 -0
README.md ADDED
@@ -0,0 +1,615 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ language:
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+ - en
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+ tags:
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+ - sentence-transformers
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+ - cross-encoder
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+ - reranker
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+ - generated_from_trainer
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+ - dataset_size:78704
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+ - loss:ListNetLoss
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+ base_model: jhu-clsp/ettin-encoder-150m
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+ datasets:
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+ - microsoft/ms_marco
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+ pipeline_tag: text-ranking
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+ library_name: sentence-transformers
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+ ---
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+
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+ # CrossEncoder based on jhu-clsp/ettin-encoder-150m
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+
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+ This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/ettin-encoder-150m](https://huggingface.co/jhu-clsp/ettin-encoder-150m) on the [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Cross Encoder
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+ - **Base model:** [jhu-clsp/ettin-encoder-150m](https://huggingface.co/jhu-clsp/ettin-encoder-150m) <!-- at revision 45d08642849e5c5701b162671ac811b7654bfd9f -->
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+ - **Maximum Sequence Length:** 7999 tokens
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+ - **Number of Output Labels:** 1 label
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+ - **Training Dataset:**
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+ - [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco)
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+ - **Language:** en
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import CrossEncoder
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+
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+ # Download from the 🤗 Hub
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+ model = CrossEncoder("bansalaman18/reranker-msmarco-v1.1-ettin-encoder-150m-listnet")
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+ # Get scores for pairs of texts
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+ pairs = [
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+ ['what is the definition of Chirality', 'Two enantiomers of a generic amino acid that is chiral. Chirality /kaɪˈraelɪtiː/ kaɪˈrælɪtiː is a property of symmetry important in several branches of. Science the word chirality is derived from The, greek (χειρ), kheir, hand a familiar chiral. object An object or a system is chiral if it is distinguishable from its mirror image; that is, it cannot be superposed onto it. Conversely, a mirror image of an achiral object, such as a sphere, cannot be distinguished from the object.'],
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+ ['what is the definition of Chirality', 'The symmetry of a molecule (or any other object) determines whether it is chiral. A molecule is achiral (not chiral) when an improper rotation, that is a combination of a rotation and a reflection in a plane, perpendicular to the axis of rotation, results in the same molecule. For tetrahedral molecules, the molecule is chiral if all four substituents are different. Chirality is important in context of ordered phases as well, for example the addition of a small amount of an optically active molecule to a nematic phase (a phase that has long range orientational order of molecules) transforms that phase to a chiral nematic phase (or cholesteric phase).'],
61
+ ['what is the definition of Chirality', 'Chirality is when an object is present in two forms which are mirror images of each other. The two forms cannot be superimposed. Human hands are chiral. The left hand is a mirror image of the right hand. You cannot turn or move one of your hands to look exactly the same as the other. In chemistry, chirality refers to molecules. If a molecule is chiral it has two forms that are mirror images of each other but otherwise identical. Chemists call these enantiomers or optical isomers.'],
62
+ ['what is the definition of Chirality', 'Chirality /kaɪˈraelɪtiː/ kaɪˈrælɪtiː is a property of symmetry important in several branches of. Science the word chirality is derived from The, greek (χειρ), kheir, hand a familiar chiral. Object an object or a system is chiral if it is distinguishable from its mirror; image that, is it cannot be superposed onto. it An object or a system is chiral if it is distinguishable from its mirror image; that is, it cannot be superposed onto it. Conversely, a mirror image of an achiral object, such as a sphere, cannot be distinguished from the object.'],
63
+ ['what is the definition of Chirality', 'Normally, when a tetrahedral atom has four different substituents it is chiral. However, in rare cases, if two of the ligands differ from each other by being mirror images of each other, the mirror image of the molecule is identical to the original, and the molecule is achiral. Chirality is important in context of ordered phases as well, for example the addition of a small amount of an optically active molecule to a nematic phase (a phase that has long range orientational order of molecules) transforms that phase to a chiral nematic phase (or cholesteric phase).'],
64
+ ]
65
+ scores = model.predict(pairs)
66
+ print(scores.shape)
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+ # (5,)
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+
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+ # Or rank different texts based on similarity to a single text
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+ ranks = model.rank(
71
+ 'what is the definition of Chirality',
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+ [
73
+ 'Two enantiomers of a generic amino acid that is chiral. Chirality /kaɪˈraelɪtiː/ kaɪˈrælɪtiː is a property of symmetry important in several branches of. Science the word chirality is derived from The, greek (χειρ), kheir, hand a familiar chiral. object An object or a system is chiral if it is distinguishable from its mirror image; that is, it cannot be superposed onto it. Conversely, a mirror image of an achiral object, such as a sphere, cannot be distinguished from the object.',
74
+ 'The symmetry of a molecule (or any other object) determines whether it is chiral. A molecule is achiral (not chiral) when an improper rotation, that is a combination of a rotation and a reflection in a plane, perpendicular to the axis of rotation, results in the same molecule. For tetrahedral molecules, the molecule is chiral if all four substituents are different. Chirality is important in context of ordered phases as well, for example the addition of a small amount of an optically active molecule to a nematic phase (a phase that has long range orientational order of molecules) transforms that phase to a chiral nematic phase (or cholesteric phase).',
75
+ 'Chirality is when an object is present in two forms which are mirror images of each other. The two forms cannot be superimposed. Human hands are chiral. The left hand is a mirror image of the right hand. You cannot turn or move one of your hands to look exactly the same as the other. In chemistry, chirality refers to molecules. If a molecule is chiral it has two forms that are mirror images of each other but otherwise identical. Chemists call these enantiomers or optical isomers.',
76
+ 'Chirality /kaɪˈraelɪtiː/ kaɪˈrælɪtiː is a property of symmetry important in several branches of. Science the word chirality is derived from The, greek (χειρ), kheir, hand a familiar chiral. Object an object or a system is chiral if it is distinguishable from its mirror; image that, is it cannot be superposed onto. it An object or a system is chiral if it is distinguishable from its mirror image; that is, it cannot be superposed onto it. Conversely, a mirror image of an achiral object, such as a sphere, cannot be distinguished from the object.',
77
+ 'Normally, when a tetrahedral atom has four different substituents it is chiral. However, in rare cases, if two of the ligands differ from each other by being mirror images of each other, the mirror image of the molecule is identical to the original, and the molecule is achiral. Chirality is important in context of ordered phases as well, for example the addition of a small amount of an optically active molecule to a nematic phase (a phase that has long range orientational order of molecules) transforms that phase to a chiral nematic phase (or cholesteric phase).',
78
+ ]
79
+ )
80
+ # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
81
+ ```
82
+
83
+ <!--
84
+ ### Direct Usage (Transformers)
85
+
86
+ <details><summary>Click to see the direct usage in Transformers</summary>
87
+
88
+ </details>
89
+ -->
90
+
91
+ <!--
92
+ ### Downstream Usage (Sentence Transformers)
93
+
94
+ You can finetune this model on your own dataset.
95
+
96
+ <details><summary>Click to expand</summary>
97
+
98
+ </details>
99
+ -->
100
+
101
+ <!--
102
+ ### Out-of-Scope Use
103
+
104
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
105
+ -->
106
+
107
+ <!--
108
+ ## Bias, Risks and Limitations
109
+
110
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
111
+ -->
112
+
113
+ <!--
114
+ ### Recommendations
115
+
116
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
117
+ -->
118
+
119
+ ## Training Details
120
+
121
+ ### Training Dataset
122
+
123
+ #### ms_marco
124
+
125
+ * Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
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+ * Size: 78,704 training samples
127
+ * Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
128
+ * Approximate statistics based on the first 1000 samples:
129
+ | | query | docs | labels |
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+ |:--------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
131
+ | type | string | list | list |
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+ | details | <ul><li>min: 11 characters</li><li>mean: 34.52 characters</li><li>max: 112 characters</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> |
133
+ * Samples:
134
+ | query | docs | labels |
135
+ |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
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+ | <code>what tissues does the respiratory membrane consist of</code> | <code>['Confidence votes 2.3K. The respiratory membrane consists of the epithelial cells of the alveolus, the endothelial cells of the capillary, and the two fused basement membranes of these layers. ', 'Membranes. Body membranes are thin sheets of tissue that cover the body, line body cavities, and cover organs within the cavities in hollow organs. They can be categorized into epithelial and connective tissue membrane. Synovial membranes are connective tissue membranes that line the cavities of the freely movable joints such as the shoulder, elbow, and knee. Like serous membranes, they line cavities that do not open to the outside.', 'On another level, epithelia and connective tissues combine to form membranes that cover and protect other structures and tissues. Types of membranes: 1 Mucous membranes line cavities that connect with the exterior, including the digestive, respiratory, reproductive, and urinary tracts. 2 The epithelial surfaces are kept moist at all times. 3 The connective ...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
137
+ | <code>where is the human stomach located</code> | <code>['All rights reserved. The stomach is a muscular organ located on the left side of the upper abdomen. The stomach receives food from the esophagus. As food reaches the end of the esophagus, it enters the stomach through a muscular valve called the lower esophageal sphincter. The stomach secretes acid and enzymes that digest food. ', 'Stomach. The stomach is a muscular, J-shaped organ in the abdomen. Part of the digestive system, the stomach stores and digests food through gastric juices and a specialized churning action created by folds on its inside. The stomach is located in the upper-left area of the abdomen below the liver and next to the spleen. It’s main function is store and breakdown foods and liquids that we consume before those contents travel to other organs to be further digested. When the stomach is empty, the inside has small folds called rugae.', 'The. Encyclopaedia Encyclopædia, Britannica. Inc the human stomach is Roughly-j shaped and is located in the upper left side ...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
138
+ | <code>brick wall cost per linear foot</code> | <code>['The garden is to be enclosed by a stone wall costing $26 per linear foot. The interior wall is to be constructed with brick costing $8 per linear foot. I need to make a quote for a garden wall. The wall is to be one brick thick, 8 metres long and 0.75 meters high above ground level. the ground in the area is good quality top soil to a depth of 500mm and below is solid chalk.', 'Our free calculator uses recent, trusted data to estimate costs for your Concrete Block Wall Cap Installation project. For a basic 25 feet project in zip code 47474, the benchmark cost to Install a Concrete Block Wall Cap ranges between $13.26 - $16.36 per linear foot. To estimate costs for your project: 1. Set Project Zip Code Enter the Zip Code for the location where labor is hired and materials purchased. 2.', 'The cost of an eight foot tall brick fence will be $7.50 a linear foot. This cost is inclusive of material only. It is always best to consult a local building contractor to get a proper estimate of t...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
139
+ * Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
140
+ ```json
141
+ {
142
+ "activation_fn": "torch.nn.modules.linear.Identity",
143
+ "mini_batch_size": 16
144
+ }
145
+ ```
146
+
147
+ ### Evaluation Dataset
148
+
149
+ #### ms_marco
150
+
151
+ * Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
152
+ * Size: 1,000 evaluation samples
153
+ * Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
154
+ * Approximate statistics based on the first 1000 samples:
155
+ | | query | docs | labels |
156
+ |:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
157
+ | type | string | list | list |
158
+ | details | <ul><li>min: 11 characters</li><li>mean: 33.78 characters</li><li>max: 91 characters</li></ul> | <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> | <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> |
159
+ * Samples:
160
+ | query | docs | labels |
161
+ |:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
162
+ | <code>what is the definition of Chirality</code> | <code>['Two enantiomers of a generic amino acid that is chiral. Chirality /kaɪˈraelɪtiː/ kaɪˈrælɪtiː is a property of symmetry important in several branches of. Science the word chirality is derived from The, greek (χειρ), kheir, hand a familiar chiral. object An object or a system is chiral if it is distinguishable from its mirror image; that is, it cannot be superposed onto it. Conversely, a mirror image of an achiral object, such as a sphere, cannot be distinguished from the object.', 'The symmetry of a molecule (or any other object) determines whether it is chiral. A molecule is achiral (not chiral) when an improper rotation, that is a combination of a rotation and a reflection in a plane, perpendicular to the axis of rotation, results in the same molecule. For tetrahedral molecules, the molecule is chiral if all four substituents are different. Chirality is important in context of ordered phases as well, for example the addition of a small amount of an optically active molecule to a nem...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
163
+ | <code>what is secularism</code> | <code>['Secularism is a code of duty pertaining to this life, founded on considerations purely human, and intended mainly for those who find theology indefinite or inadequate, unreliable or unbelievable. Its essential principles are three: (1) The improvement of this life by material means. In political terms, secularism is a movement towards the separation of religion and government (often termed the separation of church and state).', 'Secularism is about democracy and fairness. In a secular democracy all citizens are equal before the law and parliament. No religious or political affiliation gives advantages or disadvantages and religious believers are citizens with the same rights and obligations as anyone else. Secularism champions human rights above discriminatory religious demands. Secularism seeks to ensure and protect freedom of religious belief and practice for all citizens. Secularism is not about curtailing religious freedoms; it is about ensuring that the freedoms of thought and c...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
164
+ | <code>possible permanent side effects of testosterone injections</code> | <code>['Common side effects of Depo-Testosterone include nausea, vomiting, headache, anxiety, depression, skin color changes, increased/decreased sexual interest, oily skin, hair loss, breast swelling in men, numbness or tingly feeling, and acne. Pain, swelling, and redness at the injection site may also occur. This is not a complete list of side effects and others may occur. Tell your doctor about any unusual or bothersome side effect. You may report side effects to FDA at 1-800-FDA-1088. Read the entire detailed patient monograph for Depo-Testosterone (Testosterone Cypionate Injection).', 'What causes side effects. Hormone therapies for prostate cancer cause side effects because they reduce the levels of male hormones in the body. Some side effects are common to all hormone therapies used for prostate cancer. Some effects vary from drug to drug', 'Testosterone replacement therapy is designed to restore your testosterone to normal levels. It is administered through injections, patches, or g...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
165
+ * Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
166
+ ```json
167
+ {
168
+ "activation_fn": "torch.nn.modules.linear.Identity",
169
+ "mini_batch_size": 16
170
+ }
171
+ ```
172
+
173
+ ### Training Hyperparameters
174
+ #### Non-Default Hyperparameters
175
+
176
+ - `eval_strategy`: steps
177
+ - `per_device_train_batch_size`: 16
178
+ - `per_device_eval_batch_size`: 16
179
+ - `learning_rate`: 2e-05
180
+ - `num_train_epochs`: 5
181
+ - `seed`: 12
182
+ - `bf16`: True
183
+ - `load_best_model_at_end`: True
184
+
185
+ #### All Hyperparameters
186
+ <details><summary>Click to expand</summary>
187
+
188
+ - `overwrite_output_dir`: False
189
+ - `do_predict`: False
190
+ - `eval_strategy`: steps
191
+ - `prediction_loss_only`: True
192
+ - `per_device_train_batch_size`: 16
193
+ - `per_device_eval_batch_size`: 16
194
+ - `per_gpu_train_batch_size`: None
195
+ - `per_gpu_eval_batch_size`: None
196
+ - `gradient_accumulation_steps`: 1
197
+ - `eval_accumulation_steps`: None
198
+ - `torch_empty_cache_steps`: None
199
+ - `learning_rate`: 2e-05
200
+ - `weight_decay`: 0.0
201
+ - `adam_beta1`: 0.9
202
+ - `adam_beta2`: 0.999
203
+ - `adam_epsilon`: 1e-08
204
+ - `max_grad_norm`: 1.0
205
+ - `num_train_epochs`: 5
206
+ - `max_steps`: -1
207
+ - `lr_scheduler_type`: linear
208
+ - `lr_scheduler_kwargs`: {}
209
+ - `warmup_ratio`: 0.0
210
+ - `warmup_steps`: 0
211
+ - `log_level`: passive
212
+ - `log_level_replica`: warning
213
+ - `log_on_each_node`: True
214
+ - `logging_nan_inf_filter`: True
215
+ - `save_safetensors`: True
216
+ - `save_on_each_node`: False
217
+ - `save_only_model`: False
218
+ - `restore_callback_states_from_checkpoint`: False
219
+ - `no_cuda`: False
220
+ - `use_cpu`: False
221
+ - `use_mps_device`: False
222
+ - `seed`: 12
223
+ - `data_seed`: None
224
+ - `jit_mode_eval`: False
225
+ - `use_ipex`: False
226
+ - `bf16`: True
227
+ - `fp16`: False
228
+ - `fp16_opt_level`: O1
229
+ - `half_precision_backend`: auto
230
+ - `bf16_full_eval`: False
231
+ - `fp16_full_eval`: False
232
+ - `tf32`: None
233
+ - `local_rank`: 0
234
+ - `ddp_backend`: None
235
+ - `tpu_num_cores`: None
236
+ - `tpu_metrics_debug`: False
237
+ - `debug`: []
238
+ - `dataloader_drop_last`: False
239
+ - `dataloader_num_workers`: 0
240
+ - `dataloader_prefetch_factor`: None
241
+ - `past_index`: -1
242
+ - `disable_tqdm`: False
243
+ - `remove_unused_columns`: True
244
+ - `label_names`: None
245
+ - `load_best_model_at_end`: True
246
+ - `ignore_data_skip`: False
247
+ - `fsdp`: []
248
+ - `fsdp_min_num_params`: 0
249
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
250
+ - `tp_size`: 0
251
+ - `fsdp_transformer_layer_cls_to_wrap`: None
252
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
253
+ - `deepspeed`: None
254
+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
257
+ - `adafactor`: False
258
+ - `group_by_length`: False
259
+ - `length_column_name`: length
260
+ - `ddp_find_unused_parameters`: None
261
+ - `ddp_bucket_cap_mb`: None
262
+ - `ddp_broadcast_buffers`: False
263
+ - `dataloader_pin_memory`: True
264
+ - `dataloader_persistent_workers`: False
265
+ - `skip_memory_metrics`: True
266
+ - `use_legacy_prediction_loop`: False
267
+ - `push_to_hub`: False
268
+ - `resume_from_checkpoint`: None
269
+ - `hub_model_id`: None
270
+ - `hub_strategy`: every_save
271
+ - `hub_private_repo`: None
272
+ - `hub_always_push`: False
273
+ - `gradient_checkpointing`: False
274
+ - `gradient_checkpointing_kwargs`: None
275
+ - `include_inputs_for_metrics`: False
276
+ - `include_for_metrics`: []
277
+ - `eval_do_concat_batches`: True
278
+ - `fp16_backend`: auto
279
+ - `push_to_hub_model_id`: None
280
+ - `push_to_hub_organization`: None
281
+ - `mp_parameters`:
282
+ - `auto_find_batch_size`: False
283
+ - `full_determinism`: False
284
+ - `torchdynamo`: None
285
+ - `ray_scope`: last
286
+ - `ddp_timeout`: 1800
287
+ - `torch_compile`: False
288
+ - `torch_compile_backend`: None
289
+ - `torch_compile_mode`: None
290
+ - `include_tokens_per_second`: False
291
+ - `include_num_input_tokens_seen`: False
292
+ - `neftune_noise_alpha`: None
293
+ - `optim_target_modules`: None
294
+ - `batch_eval_metrics`: False
295
+ - `eval_on_start`: False
296
+ - `use_liger_kernel`: False
297
+ - `eval_use_gather_object`: False
298
+ - `average_tokens_across_devices`: False
299
+ - `prompts`: None
300
+ - `batch_sampler`: batch_sampler
301
+ - `multi_dataset_batch_sampler`: proportional
302
+ - `router_mapping`: {}
303
+ - `learning_rate_mapping`: {}
304
+
305
+ </details>
306
+
307
+ ### Training Logs
308
+ <details><summary>Click to expand</summary>
309
+
310
+ | Epoch | Step | Training Loss | Validation Loss |
311
+ |:----------:|:--------:|:-------------:|:---------------:|
312
+ | 0.0002 | 1 | 2.0508 | - |
313
+ | 0.0203 | 100 | 2.0905 | 2.0813 |
314
+ | 0.0407 | 200 | 2.0766 | 2.0785 |
315
+ | 0.0610 | 300 | 2.0884 | 2.0743 |
316
+ | 0.0813 | 400 | 2.0738 | 2.0717 |
317
+ | 0.1016 | 500 | 2.0676 | 2.0702 |
318
+ | 0.1220 | 600 | 2.0728 | 2.0696 |
319
+ | 0.1423 | 700 | 2.0738 | 2.0684 |
320
+ | 0.1626 | 800 | 2.0699 | 2.0685 |
321
+ | 0.1830 | 900 | 2.076 | 2.0677 |
322
+ | 0.2033 | 1000 | 2.0652 | 2.0672 |
323
+ | 0.2236 | 1100 | 2.0695 | 2.0673 |
324
+ | 0.2440 | 1200 | 2.0762 | 2.0674 |
325
+ | 0.2643 | 1300 | 2.0673 | 2.0673 |
326
+ | 0.2846 | 1400 | 2.07 | 2.0670 |
327
+ | 0.3049 | 1500 | 2.0751 | 2.0664 |
328
+ | 0.3253 | 1600 | 2.0785 | 2.0664 |
329
+ | 0.3456 | 1700 | 2.0732 | 2.0663 |
330
+ | 0.3659 | 1800 | 2.0793 | 2.0659 |
331
+ | 0.3863 | 1900 | 2.0739 | 2.0664 |
332
+ | 0.4066 | 2000 | 2.0715 | 2.0658 |
333
+ | 0.4269 | 2100 | 2.0693 | 2.0663 |
334
+ | 0.4472 | 2200 | 2.0735 | 2.0659 |
335
+ | 0.4676 | 2300 | 2.0787 | 2.0652 |
336
+ | 0.4879 | 2400 | 2.0748 | 2.0650 |
337
+ | 0.5082 | 2500 | 2.0683 | 2.0649 |
338
+ | 0.5286 | 2600 | 2.0693 | 2.0648 |
339
+ | 0.5489 | 2700 | 2.063 | 2.0644 |
340
+ | 0.5692 | 2800 | 2.0729 | 2.0646 |
341
+ | 0.5896 | 2900 | 2.067 | 2.0650 |
342
+ | 0.6099 | 3000 | 2.0696 | 2.0644 |
343
+ | 0.6302 | 3100 | 2.0676 | 2.0648 |
344
+ | 0.6505 | 3200 | 2.0724 | 2.0651 |
345
+ | 0.6709 | 3300 | 2.0698 | 2.0650 |
346
+ | 0.6912 | 3400 | 2.071 | 2.0653 |
347
+ | 0.7115 | 3500 | 2.0713 | 2.0649 |
348
+ | 0.7319 | 3600 | 2.068 | 2.0644 |
349
+ | 0.7522 | 3700 | 2.0736 | 2.0644 |
350
+ | 0.7725 | 3800 | 2.079 | 2.0639 |
351
+ | 0.7928 | 3900 | 2.0716 | 2.0640 |
352
+ | 0.8132 | 4000 | 2.0747 | 2.0640 |
353
+ | 0.8335 | 4100 | 2.0715 | 2.0642 |
354
+ | 0.8538 | 4200 | 2.0724 | 2.0642 |
355
+ | 0.8742 | 4300 | 2.0686 | 2.0641 |
356
+ | 0.8945 | 4400 | 2.0714 | 2.0643 |
357
+ | 0.9148 | 4500 | 2.0731 | 2.0639 |
358
+ | 0.9351 | 4600 | 2.0696 | 2.0640 |
359
+ | 0.9555 | 4700 | 2.0656 | 2.0638 |
360
+ | 0.9758 | 4800 | 2.0719 | 2.0638 |
361
+ | **0.9961** | **4900** | **2.0642** | **2.0636** |
362
+ | 1.0165 | 5000 | 2.067 | 2.0639 |
363
+ | 1.0368 | 5100 | 2.0655 | 2.0645 |
364
+ | 1.0571 | 5200 | 2.0665 | 2.0648 |
365
+ | 1.0775 | 5300 | 2.0653 | 2.0651 |
366
+ | 1.0978 | 5400 | 2.0609 | 2.0649 |
367
+ | 1.1181 | 5500 | 2.0572 | 2.0663 |
368
+ | 1.1384 | 5600 | 2.0667 | 2.0660 |
369
+ | 1.1588 | 5700 | 2.0639 | 2.0652 |
370
+ | 1.1791 | 5800 | 2.06 | 2.0655 |
371
+ | 1.1994 | 5900 | 2.0566 | 2.0649 |
372
+ | 1.2198 | 6000 | 2.0735 | 2.0649 |
373
+ | 1.2401 | 6100 | 2.0657 | 2.0658 |
374
+ | 1.2604 | 6200 | 2.0621 | 2.0648 |
375
+ | 1.2807 | 6300 | 2.0659 | 2.0650 |
376
+ | 1.3011 | 6400 | 2.0591 | 2.0654 |
377
+ | 1.3214 | 6500 | 2.0674 | 2.0648 |
378
+ | 1.3417 | 6600 | 2.0586 | 2.0648 |
379
+ | 1.3621 | 6700 | 2.0627 | 2.0648 |
380
+ | 1.3824 | 6800 | 2.0667 | 2.0647 |
381
+ | 1.4027 | 6900 | 2.0616 | 2.0649 |
382
+ | 1.4231 | 7000 | 2.0613 | 2.0659 |
383
+ | 1.4434 | 7100 | 2.0674 | 2.0654 |
384
+ | 1.4637 | 7200 | 2.0622 | 2.0649 |
385
+ | 1.4840 | 7300 | 2.0636 | 2.0654 |
386
+ | 1.5044 | 7400 | 2.0751 | 2.0644 |
387
+ | 1.5247 | 7500 | 2.062 | 2.0646 |
388
+ | 1.5450 | 7600 | 2.067 | 2.0642 |
389
+ | 1.5654 | 7700 | 2.0722 | 2.0643 |
390
+ | 1.5857 | 7800 | 2.0651 | 2.0645 |
391
+ | 1.6060 | 7900 | 2.0654 | 2.0643 |
392
+ | 1.6263 | 8000 | 2.072 | 2.0648 |
393
+ | 1.6467 | 8100 | 2.0608 | 2.0640 |
394
+ | 1.6670 | 8200 | 2.0597 | 2.0642 |
395
+ | 1.6873 | 8300 | 2.0661 | 2.0645 |
396
+ | 1.7077 | 8400 | 2.0712 | 2.0637 |
397
+ | 1.7280 | 8500 | 2.0573 | 2.0644 |
398
+ | 1.7483 | 8600 | 2.0656 | 2.0642 |
399
+ | 1.7687 | 8700 | 2.066 | 2.0639 |
400
+ | 1.7890 | 8800 | 2.0656 | 2.0645 |
401
+ | 1.8093 | 8900 | 2.0704 | 2.0645 |
402
+ | 1.8296 | 9000 | 2.0649 | 2.0646 |
403
+ | 1.8500 | 9100 | 2.0699 | 2.0645 |
404
+ | 1.8703 | 9200 | 2.0598 | 2.0654 |
405
+ | 1.8906 | 9300 | 2.0742 | 2.0643 |
406
+ | 1.9110 | 9400 | 2.0596 | 2.0638 |
407
+ | 1.9313 | 9500 | 2.052 | 2.0642 |
408
+ | 1.9516 | 9600 | 2.0582 | 2.0656 |
409
+ | 1.9719 | 9700 | 2.0686 | 2.0647 |
410
+ | 1.9923 | 9800 | 2.0625 | 2.0642 |
411
+ | 2.0126 | 9900 | 2.0558 | 2.0672 |
412
+ | 2.0329 | 10000 | 2.058 | 2.0690 |
413
+ | 2.0533 | 10100 | 2.0551 | 2.0695 |
414
+ | 2.0736 | 10200 | 2.0553 | 2.0673 |
415
+ | 2.0939 | 10300 | 2.0497 | 2.0685 |
416
+ | 2.1143 | 10400 | 2.0504 | 2.0697 |
417
+ | 2.1346 | 10500 | 2.0447 | 2.0695 |
418
+ | 2.1549 | 10600 | 2.0488 | 2.0695 |
419
+ | 2.1752 | 10700 | 2.0366 | 2.0678 |
420
+ | 2.1956 | 10800 | 2.0566 | 2.0682 |
421
+ | 2.2159 | 10900 | 2.0512 | 2.0699 |
422
+ | 2.2362 | 11000 | 2.0515 | 2.0697 |
423
+ | 2.2566 | 11100 | 2.0534 | 2.0686 |
424
+ | 2.2769 | 11200 | 2.0594 | 2.0690 |
425
+ | 2.2972 | 11300 | 2.0484 | 2.0695 |
426
+ | 2.3175 | 11400 | 2.0511 | 2.0692 |
427
+ | 2.3379 | 11500 | 2.0497 | 2.0698 |
428
+ | 2.3582 | 11600 | 2.0507 | 2.0696 |
429
+ | 2.3785 | 11700 | 2.0445 | 2.0695 |
430
+ | 2.3989 | 11800 | 2.0504 | 2.0695 |
431
+ | 2.4192 | 11900 | 2.0548 | 2.0684 |
432
+ | 2.4395 | 12000 | 2.0449 | 2.0698 |
433
+ | 2.4598 | 12100 | 2.0601 | 2.0697 |
434
+ | 2.4802 | 12200 | 2.0446 | 2.0692 |
435
+ | 2.5005 | 12300 | 2.0414 | 2.0718 |
436
+ | 2.5208 | 12400 | 2.0618 | 2.0681 |
437
+ | 2.5412 | 12500 | 2.0601 | 2.0692 |
438
+ | 2.5615 | 12600 | 2.0496 | 2.0711 |
439
+ | 2.5818 | 12700 | 2.0554 | 2.0709 |
440
+ | 2.6022 | 12800 | 2.0409 | 2.0687 |
441
+ | 2.6225 | 12900 | 2.0408 | 2.0720 |
442
+ | 2.6428 | 13000 | 2.056 | 2.0719 |
443
+ | 2.6631 | 13100 | 2.0492 | 2.0685 |
444
+ | 2.6835 | 13200 | 2.0514 | 2.0687 |
445
+ | 2.7038 | 13300 | 2.0569 | 2.0698 |
446
+ | 2.7241 | 13400 | 2.0464 | 2.0698 |
447
+ | 2.7445 | 13500 | 2.0581 | 2.0720 |
448
+ | 2.7648 | 13600 | 2.0522 | 2.0717 |
449
+ | 2.7851 | 13700 | 2.05 | 2.0720 |
450
+ | 2.8054 | 13800 | 2.0421 | 2.0695 |
451
+ | 2.8258 | 13900 | 2.0503 | 2.0707 |
452
+ | 2.8461 | 14000 | 2.0446 | 2.0689 |
453
+ | 2.8664 | 14100 | 2.0507 | 2.0695 |
454
+ | 2.8868 | 14200 | 2.0501 | 2.0700 |
455
+ | 2.9071 | 14300 | 2.0569 | 2.0688 |
456
+ | 2.9274 | 14400 | 2.0574 | 2.0689 |
457
+ | 2.9478 | 14500 | 2.0489 | 2.0701 |
458
+ | 2.9681 | 14600 | 2.0532 | 2.0700 |
459
+ | 2.9884 | 14700 | 2.0552 | 2.0699 |
460
+ | 3.0087 | 14800 | 2.046 | 2.0751 |
461
+ | 3.0291 | 14900 | 2.0256 | 2.0747 |
462
+ | 3.0494 | 15000 | 2.0334 | 2.0753 |
463
+ | 3.0697 | 15100 | 2.0359 | 2.0773 |
464
+ | 3.0901 | 15200 | 2.0256 | 2.0750 |
465
+ | 3.1104 | 15300 | 2.0296 | 2.0749 |
466
+ | 3.1307 | 15400 | 2.0353 | 2.0748 |
467
+ | 3.1510 | 15500 | 2.0256 | 2.0748 |
468
+ | 3.1714 | 15600 | 2.0295 | 2.0759 |
469
+ | 3.1917 | 15700 | 2.0238 | 2.0745 |
470
+ | 3.2120 | 15800 | 2.0259 | 2.0781 |
471
+ | 3.2324 | 15900 | 2.0373 | 2.0754 |
472
+ | 3.2527 | 16000 | 2.0358 | 2.0751 |
473
+ | 3.2730 | 16100 | 2.027 | 2.0769 |
474
+ | 3.2934 | 16200 | 2.0316 | 2.0760 |
475
+ | 3.3137 | 16300 | 2.0392 | 2.0751 |
476
+ | 3.3340 | 16400 | 2.0391 | 2.0759 |
477
+ | 3.3543 | 16500 | 2.0327 | 2.0749 |
478
+ | 3.3747 | 16600 | 2.0317 | 2.0768 |
479
+ | 3.3950 | 16700 | 2.0386 | 2.0758 |
480
+ | 3.4153 | 16800 | 2.0331 | 2.0758 |
481
+ | 3.4357 | 16900 | 2.0393 | 2.0770 |
482
+ | 3.4560 | 17000 | 2.0277 | 2.0777 |
483
+ | 3.4763 | 17100 | 2.0353 | 2.0763 |
484
+ | 3.4966 | 17200 | 2.026 | 2.0786 |
485
+ | 3.5170 | 17300 | 2.0412 | 2.0785 |
486
+ | 3.5373 | 17400 | 2.039 | 2.0776 |
487
+ | 3.5576 | 17500 | 2.0306 | 2.0772 |
488
+ | 3.5780 | 17600 | 2.0303 | 2.0774 |
489
+ | 3.5983 | 17700 | 2.0361 | 2.0751 |
490
+ | 3.6186 | 17800 | 2.0366 | 2.0751 |
491
+ | 3.6390 | 17900 | 2.0389 | 2.0772 |
492
+ | 3.6593 | 18000 | 2.0327 | 2.0765 |
493
+ | 3.6796 | 18100 | 2.036 | 2.0756 |
494
+ | 3.6999 | 18200 | 2.0258 | 2.0758 |
495
+ | 3.7203 | 18300 | 2.0256 | 2.0758 |
496
+ | 3.7406 | 18400 | 2.038 | 2.0763 |
497
+ | 3.7609 | 18500 | 2.0391 | 2.0762 |
498
+ | 3.7813 | 18600 | 2.0246 | 2.0772 |
499
+ | 3.8016 | 18700 | 2.0269 | 2.0764 |
500
+ | 3.8219 | 18800 | 2.0311 | 2.0761 |
501
+ | 3.8422 | 18900 | 2.0366 | 2.0759 |
502
+ | 3.8626 | 19000 | 2.0325 | 2.0759 |
503
+ | 3.8829 | 19100 | 2.0427 | 2.0756 |
504
+ | 3.9032 | 19200 | 2.029 | 2.0765 |
505
+ | 3.9236 | 19300 | 2.0338 | 2.0760 |
506
+ | 3.9439 | 19400 | 2.0232 | 2.0748 |
507
+ | 3.9642 | 19500 | 2.0286 | 2.0776 |
508
+ | 3.9845 | 19600 | 2.0364 | 2.0758 |
509
+ | 4.0049 | 19700 | 2.0325 | 2.0766 |
510
+ | 4.0252 | 19800 | 2.0158 | 2.0773 |
511
+ | 4.0455 | 19900 | 2.0176 | 2.0789 |
512
+ | 4.0659 | 20000 | 2.0193 | 2.0796 |
513
+ | 4.0862 | 20100 | 2.0159 | 2.0805 |
514
+ | 4.1065 | 20200 | 2.0283 | 2.0809 |
515
+ | 4.1269 | 20300 | 2.0176 | 2.0796 |
516
+ | 4.1472 | 20400 | 2.0129 | 2.0788 |
517
+ | 4.1675 | 20500 | 2.0225 | 2.0805 |
518
+ | 4.1878 | 20600 | 2.0185 | 2.0792 |
519
+ | 4.2082 | 20700 | 2.0146 | 2.0792 |
520
+ | 4.2285 | 20800 | 2.0225 | 2.0797 |
521
+ | 4.2488 | 20900 | 2.0167 | 2.0815 |
522
+ | 4.2692 | 21000 | 2.0203 | 2.0799 |
523
+ | 4.2895 | 21100 | 2.021 | 2.0807 |
524
+ | 4.3098 | 21200 | 2.0217 | 2.0804 |
525
+ | 4.3301 | 21300 | 2.0242 | 2.0802 |
526
+ | 4.3505 | 21400 | 2.0191 | 2.0807 |
527
+ | 4.3708 | 21500 | 2.0157 | 2.0794 |
528
+ | 4.3911 | 21600 | 2.0184 | 2.0814 |
529
+ | 4.4115 | 21700 | 2.0241 | 2.0802 |
530
+ | 4.4318 | 21800 | 2.0162 | 2.0801 |
531
+ | 4.4521 | 21900 | 2.0156 | 2.0808 |
532
+ | 4.4725 | 22000 | 2.0195 | 2.0805 |
533
+ | 4.4928 | 22100 | 2.0147 | 2.0817 |
534
+ | 4.5131 | 22200 | 2.0247 | 2.0804 |
535
+ | 4.5334 | 22300 | 2.019 | 2.0815 |
536
+ | 4.5538 | 22400 | 2.0155 | 2.0806 |
537
+ | 4.5741 | 22500 | 2.0177 | 2.0810 |
538
+ | 4.5944 | 22600 | 2.0285 | 2.0809 |
539
+ | 4.6148 | 22700 | 2.0239 | 2.0805 |
540
+ | 4.6351 | 22800 | 2.0288 | 2.0810 |
541
+ | 4.6554 | 22900 | 2.0094 | 2.0815 |
542
+ | 4.6757 | 23000 | 2.019 | 2.0810 |
543
+ | 4.6961 | 23100 | 2.0235 | 2.0812 |
544
+ | 4.7164 | 23200 | 2.01 | 2.0807 |
545
+ | 4.7367 | 23300 | 2.0246 | 2.0812 |
546
+ | 4.7571 | 23400 | 2.0229 | 2.0812 |
547
+ | 4.7774 | 23500 | 2.0239 | 2.0806 |
548
+ | 4.7977 | 23600 | 2.0193 | 2.0806 |
549
+ | 4.8181 | 23700 | 2.0196 | 2.0808 |
550
+ | 4.8384 | 23800 | 2.0222 | 2.0805 |
551
+ | 4.8587 | 23900 | 2.0253 | 2.0813 |
552
+ | 4.8790 | 24000 | 2.0182 | 2.0812 |
553
+ | 4.8994 | 24100 | 2.0201 | 2.0811 |
554
+ | 4.9197 | 24200 | 2.0225 | 2.0807 |
555
+ | 4.9400 | 24300 | 2.0265 | 2.0813 |
556
+ | 4.9604 | 24400 | 2.0142 | 2.0809 |
557
+ | 4.9807 | 24500 | 2.0077 | 2.0806 |
558
+
559
+ * The bold row denotes the saved checkpoint.
560
+ </details>
561
+
562
+ ### Framework Versions
563
+ - Python: 3.11.13
564
+ - Sentence Transformers: 5.0.0
565
+ - Transformers: 4.51.0
566
+ - PyTorch: 2.9.1+cu126
567
+ - Accelerate: 1.8.1
568
+ - Datasets: 3.6.0
569
+ - Tokenizers: 0.21.4-dev.0
570
+
571
+ ## Citation
572
+
573
+ ### BibTeX
574
+
575
+ #### Sentence Transformers
576
+ ```bibtex
577
+ @inproceedings{reimers-2019-sentence-bert,
578
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
579
+ author = "Reimers, Nils and Gurevych, Iryna",
580
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
581
+ month = "11",
582
+ year = "2019",
583
+ publisher = "Association for Computational Linguistics",
584
+ url = "https://arxiv.org/abs/1908.10084",
585
+ }
586
+ ```
587
+
588
+ #### ListNetLoss
589
+ ```bibtex
590
+ @inproceedings{cao2007learning,
591
+ title={Learning to Rank: From Pairwise Approach to Listwise Approach},
592
+ author={Cao, Zhe and Qin, Tao and Liu, Tie-Yan and Tsai, Ming-Feng and Li, Hang},
593
+ booktitle={Proceedings of the 24th international conference on Machine learning},
594
+ pages={129--136},
595
+ year={2007}
596
+ }
597
+ ```
598
+
599
+ <!--
600
+ ## Glossary
601
+
602
+ *Clearly define terms in order to be accessible across audiences.*
603
+ -->
604
+
605
+ <!--
606
+ ## Model Card Authors
607
+
608
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
609
+ -->
610
+
611
+ <!--
612
+ ## Model Card Contact
613
+
614
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
615
+ -->
config.json ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "ModernBertForSequenceClassification"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "bos_token_id": 50281,
8
+ "causal_mask": false,
9
+ "classifier_activation": "gelu",
10
+ "classifier_bias": false,
11
+ "classifier_dropout": 0.0,
12
+ "classifier_pooling": "mean",
13
+ "cls_token_id": 50281,
14
+ "decoder_bias": true,
15
+ "deterministic_flash_attn": false,
16
+ "embedding_dropout": 0.0,
17
+ "eos_token_id": 50282,
18
+ "global_attn_every_n_layers": 3,
19
+ "global_rope_theta": 160000.0,
20
+ "gradient_checkpointing": false,
21
+ "hidden_activation": "gelu",
22
+ "hidden_size": 768,
23
+ "id2label": {
24
+ "0": "LABEL_0"
25
+ },
26
+ "initializer_cutoff_factor": 2.0,
27
+ "initializer_range": 0.02,
28
+ "intermediate_size": 1152,
29
+ "is_causal": false,
30
+ "label2id": {
31
+ "LABEL_0": 0
32
+ },
33
+ "layer_norm_eps": 1e-05,
34
+ "local_attention": 128,
35
+ "local_rope_theta": 160000.0,
36
+ "max_position_embeddings": 7999,
37
+ "mlp_bias": false,
38
+ "mlp_dropout": 0.0,
39
+ "model_type": "modernbert",
40
+ "norm_bias": false,
41
+ "norm_eps": 1e-05,
42
+ "num_attention_heads": 12,
43
+ "num_hidden_layers": 22,
44
+ "pad_token_id": 50283,
45
+ "position_embedding_type": "sans_pos",
46
+ "repad_logits_with_grad": false,
47
+ "sentence_transformers": {
48
+ "activation_fn": "torch.nn.modules.activation.Sigmoid",
49
+ "version": "5.0.0"
50
+ },
51
+ "sep_token_id": 50282,
52
+ "sparse_pred_ignore_index": -100,
53
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