Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:99980
+- loss:ContrastiveTensionLoss
+base_model: sucharush/e5_stem_finetuned
+widget:
+- source_sentence: 'Question: who sang on the street where you live in my fair lady,
+    Answer: On the Street Where You Live "On the Street Where You Live" is a song
+    with music by Frederick Loewe and lyrics by Alan Jay Lerner, from the 1956 Broadway
+    musical, My Fair Lady.[1] It is sung in the musical by the character Freddy Eynsford-Hill,
+    who was portrayed by John Michael King in the original production. In the 1964
+    film version, it was sung by Bill Shirley, dubbing for actor Jeremy Brett.'
+  sentences:
+  - 'Question: who sang on the street where you live in my fair lady, Answer: On the
+    Street Where You Live "On the Street Where You Live" is a song with music by Frederick
+    Loewe and lyrics by Alan Jay Lerner, from the 1956 Broadway musical, My Fair Lady.[1]
+    It is sung in the musical by the character Freddy Eynsford-Hill, who was portrayed
+    by John Michael King in the original production. In the 1964 film version, it
+    was sung by Bill Shirley, dubbing for actor Jeremy Brett.'
+  - 'Question: who sings the song middle finger in the air, Answer: Middle Finger
+    (song) "Middle Finger" is a song by American synthpop band Cobra Starship. The
+    track was written and produced by Norwegian production team Stargate and features
+    Pittsburgh rapper Mac Miller. It was released as the third promotional single
+    from their fourth studio album, "Night Shades" and later as the second official
+    single.'
+  - '[{''from'': ''human'', ''value'': ''How can you use the x-intercepts and vertex
+    of a quadratic equation to sketch its graph accurately?''}, {''from'': ''gpt'',
+    ''value'': ''To sketch the graph of a quadratic equation using the x-intercepts
+    and vertex:\n\n1. Find the x-intercepts: Set y = 0 in the equation and solve for
+    x. The x-intercepts are the points where the graph crosses the x-axis.\n2. Find
+    the vertex: Convert the equation into vertex form: y = a(x-h)^2 + k, where (h,k)
+    is the vertex.\n3. Plot the x-intercepts and vertex: Mark these points on the
+    graph.\n4. Determine the shape of the parabola: If a > 0, the parabola opens upwards.
+    If a < 0, it opens downwards.\n5. Sketch the parabola: Draw a smooth curve through
+    the x-intercepts and vertex, following the shape determined in step 4.\n\nBy following
+    these steps, you can accurately sketch the graph of a quadratic equation using
+    its x-intercepts and vertex.''}]'
+- source_sentence: '[{''from'': ''human'', ''value'': ''Find the number of moles of
+    Chloromethane formed on combining 3 moles of Methane and 3 moles of Chlorine''},
+    {''from'': ''gpt'', ''value'': ''The reaction between methane (CH₄) and chlorine
+    (Cl₂) to form chloromethane (CH₃Cl) and hydrogen chloride (HCl) is typically a
+    photochemical reaction that occurs in the presence of light. The balanced chemical
+    equation for this reaction is:\n\nCH₄ + Cl₂ → CH₃Cl + HCl\n\nFrom the balanced
+    equation, we can see that 1 mole of methane reacts with 1 mole of chlorine to
+    produce 1 mole of chloromethane and 1 mole of hydrogen chloride.\n\nGiven that
+    you have 3 moles of methane and 3 moles of chlorine, the reaction would proceed
+    as follows:\n\n3 moles CH₄ + 3 moles Cl₂ → 3 moles CH₃Cl + 3 moles HCl\n\nTherefore,
+    3 moles of chloromethane would be formed from the reaction of 3 moles of methane
+    with 3 moles of chlorine, assuming the reaction goes to completion and there are
+    no side reactions.''}]'
+  sentences:
+  - '[{''from'': ''human'', ''value'': ''Find the number of moles of Chloromethane
+    formed on combining 3 moles of Methane and 3 moles of Chlorine''}, {''from'':
+    ''gpt'', ''value'': ''The reaction between methane (CH₄) and chlorine (Cl₂) to
+    form chloromethane (CH₃Cl) and hydrogen chloride (HCl) is typically a photochemical
+    reaction that occurs in the presence of light. The balanced chemical equation
+    for this reaction is:\n\nCH₄ + Cl₂ → CH₃Cl + HCl\n\nFrom the balanced equation,
+    we can see that 1 mole of methane reacts with 1 mole of chlorine to produce 1
+    mole of chloromethane and 1 mole of hydrogen chloride.\n\nGiven that you have
+    3 moles of methane and 3 moles of chlorine, the reaction would proceed as follows:\n\n3
+    moles CH₄ + 3 moles Cl₂ → 3 moles CH₃Cl + 3 moles HCl\n\nTherefore, 3 moles of
+    chloromethane would be formed from the reaction of 3 moles of methane with 3 moles
+    of chlorine, assuming the reaction goes to completion and there are no side reactions.''}]'
+  - 'Question: where was the remake of dirty dancing filmed, Answer: western North
+    Carolina'
+  - '[{''from'': ''human'', ''value'': "How can I sort the given characters ''S'',
+    ''O'', ''R'', ''T'' in alphabetical order using a Java program?"}, {''from'':
+    ''gpt'', ''value'': ''To sort the given characters in alphabetical order using
+    a Java program, you can use the following code:\n\n```java\nimport java.util.Arrays;
+    \n\npublic class Main {\n    public static void main(String[] args)\n    {\n        //
+    Given characters\n        char[] charArray = { \''S\'', \''O\'', \''R\'', \''T\''
+    };\n\n        // Sort the array\n        Arrays.sort(charArray);\n\n        //
+    Print out the sorted characters\n        System.out.print("Characters arranged
+    in alphabetical order: ");\n        for (char c : charArray) {\n            System.out.print(c
+    + " ");\n        }\n    }\n}\n```\n\nWhen you run this program, it will output:
+    "Characters arranged in alphabetical order: O R S T". \n\nIn this code, we first
+    declare an array called `charArray` which contains the given characters \''S\'',
+    \''O\'', \''R\'', \''T\''. Then, we use the `Arrays.sort()` function from Java\''s
+    `Arrays` class to sort the array in ascending order, which is the same as alphabetical
+    order for characters. Finally, we iterate over the sorted array and print each
+    character separated by a space.''}]'
+- source_sentence: 'Question: what production model was the tiger i tank of world
+    war ii, Answer: Tiger I The Tiger I  listen (help·info) is a German heavy tank
+    of World War II deployed from 1942 in Africa and Europe, usually in independent
+    heavy tank battalions. Its final designation was Panzerkampfwagen VI Tiger Ausf.
+    E often shortened to Tiger. The Tiger I gave the Wehrmacht its first armoured
+    fighting vehicle that mounted the 8.8 cm KwK 36 gun (not to be confused with the
+    8.8 cm Flak 36). 1,347 were built between August 1942 and August 1944.[9] After
+    August 1944, production of the Tiger I was phased out in favour of the Tiger II.'
+  sentences:
+  - 'Question: who sang theme song for star trek enterprise, Answer: Faith of the
+    Heart It was also recorded by English tenor Russell Watson as "Where My Heart
+    Will Take Me" in order to be used as a theme to the 2001 television series Star
+    Trek: Enterprise. This version of the single was used on four occasions as wake-up
+    calls onboard Space Shuttle missions, and performed by Watson at the 2002 Commonwealth
+    Games. Watson also recorded a special version of the song to be played for the
+    final wake up of the New Horizons exploration spacecraft on December 6, 2014.[1]'
+  - 'Question: what production model was the tiger i tank of world war ii, Answer:
+    Tiger I The Tiger I  listen (help·info) is a German heavy tank of World War II
+    deployed from 1942 in Africa and Europe, usually in independent heavy tank battalions.
+    Its final designation was Panzerkampfwagen VI Tiger Ausf. E often shortened to
+    Tiger. The Tiger I gave the Wehrmacht its first armoured fighting vehicle that
+    mounted the 8.8 cm KwK 36 gun (not to be confused with the 8.8 cm Flak 36). 1,347
+    were built between August 1942 and August 1944.[9] After August 1944, production
+    of the Tiger I was phased out in favour of the Tiger II.'
+  - 'Question: (Neural networks) Training only the first layer of a deep neural network
+    using the logistic loss is equivalent to training a logistic regression over a
+    transformed feature space., Answer: False'
+- source_sentence: 'Question: who sings prop me up by the jukebox, Answer: Prop Me
+    Up Beside the Jukebox (If I Die) "Prop Me Up Beside the Jukebox (If I Die)" is
+    a song written by Kerry Kurt Phillips, Howard Perdew and Rick Blaylock, and recorded
+    by American country music singer Joe Diffie. It was released in July 1993 as the
+    second single from his CD Honky Tonk Attitude. It peaked at number 3 on the Billboard
+    Hot Country Singles & Tracks (now Hot Country Songs) chart.'
+  sentences:
+  - '[{''from'': ''human'', ''value'': ''Explain how the weaker molecular forces in
+    steam affect its physical properties compared to liquid water.''}, {''from'':
+    ''gpt'', ''value'': ''The weaker molecular forces in steam result in a lower density,
+    higher compressibility, and lower boiling point compared to liquid water. The
+    molecules are farther apart and less attracted to each other, making steam easier
+    to compress and expand, and requiring less energy to separate into a gas phase.''}]'
+  - 'Question: what episode does justin die in chicago pd, Answer: Hank Voight In
+    "Start Digging", Hank is offered a meritorious promotion to Lieutenant by Commander
+    Emma Crowley (Barbara Eve Harris) and the CPD brass as part of the new Chicago
+    Police Superintendent''s reorganization of the department that includes getting
+    officers like him off the streets. However, before Hank could either accept or
+    decline the promotion, his son Justin was assaulted and later died of his injuries,
+    causing Hank to go on a revenge mission using whatever means necessary to find
+    Justin''s murderer. He takes the murderer to the silos, he makes him dig a hole
+    and then kills him.'
+  - 'Question: who sings prop me up by the jukebox, Answer: Prop Me Up Beside the
+    Jukebox (If I Die) "Prop Me Up Beside the Jukebox (If I Die)" is a song written
+    by Kerry Kurt Phillips, Howard Perdew and Rick Blaylock, and recorded by American
+    country music singer Joe Diffie. It was released in July 1993 as the second single
+    from his CD Honky Tonk Attitude. It peaked at number 3 on the Billboard Hot Country
+    Singles & Tracks (now Hot Country Songs) chart.'
+- source_sentence: The text presents a novel pilot-free multi-user uplink framework
+    for integrated sensing and communication (ISAC) in mm-wave networks. It enables
+    single-antenna users to transmit without dedicated pilots, allowing the base station
+    to decode messages and detect targets by utilizing users' spatial and velocity
+    diversities. The system employs a 3D super-resolution approach to jointly estimate
+    multi-path parameters like delays, Doppler shifts, and angles-of-arrival, solved
+    through semidefinite programming. A key innovation is multi-user fusion, enhancing
+    sensing and decoding by combining diverse user observations. This approach improves
+    robustness and integrates multi-user perspectives for high-resolution sensing
+    and communication. Numerical results demonstrate substantial enhancements in target
+    estimation and communication performance. The methodology transforms user transmissions
+    into valuable sensing opportunities, crucial for the next generation of wireless
+    networks focused on environmental awareness and reliable data transmission. Future
+    work includes addressing user mobility, real-time challenges, and optimizing fusion
+    techniques in noisy conditions.
+  sentences:
+  - 'Question: why did green day write when september ends, Answer: Wake Me Up When
+    September Ends "Wake Me Up When September Ends" is a song by American rock band
+    Green Day, released on June 13, 2005, as the fourth single from the group''s seventh
+    studio album, American Idiot (2004). The song was written by frontman Billie Joe
+    Armstrong regarding the death of his father.'
+  - 'Question: what is charlotte''s real name from henry danger, Answer: List of Henry
+    Danger characters Charlotte[5] (Riele Downs) is one of Henry''s best friends.
+    She is sarcastic, clever, and smart. She is the "sass master" of the bunch, always
+    there to snap everyone back to reality. She and Henry have been best friends for
+    a long time and therefore she is close enough to him to tell it like it is. She
+    is a big fan of Captain Man. In the fourth episode, Charlotte figures out Henry''s
+    secret and gets a job as Henry and Ray''s manager.'
+  - The text presents a novel pilot-free multi-user uplink framework for integrated
+    sensing and communication (ISAC) in mm-wave networks. It enables single-antenna
+    users to transmit without dedicated pilots, allowing the base station to decode
+    messages and detect targets by utilizing users' spatial and velocity diversities.
+    The system employs a 3D super-resolution approach to jointly estimate multi-path
+    parameters like delays, Doppler shifts, and angles-of-arrival, solved through
+    semidefinite programming. A key innovation is multi-user fusion, enhancing sensing
+    and decoding by combining diverse user observations. This approach improves robustness
+    and integrates multi-user perspectives for high-resolution sensing and communication.
+    Numerical results demonstrate substantial enhancements in target estimation and
+    communication performance. The methodology transforms user transmissions into
+    valuable sensing opportunities, crucial for the next generation of wireless networks
+    focused on environmental awareness and reliable data transmission. Future work
+    includes addressing user mobility, real-time challenges, and optimizing fusion
+    techniques in noisy conditions.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sucharush/e5_stem_finetuned
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sucharush/e5_stem_finetuned](https://huggingface.co/sucharush/e5_stem_finetuned). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sucharush/e5_stem_finetuned](https://huggingface.co/sucharush/e5_stem_finetuned) <!-- at revision 3013cc95b53a83082746afd556743e81f1da5dff -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Stergios-Konstantinidis/MNLP_M3_tokenizer_tuned_pos_neg_1")
+# Run inference
+sentences = [
+    "The text presents a novel pilot-free multi-user uplink framework for integrated sensing and communication (ISAC) in mm-wave networks. It enables single-antenna users to transmit without dedicated pilots, allowing the base station to decode messages and detect targets by utilizing users' spatial and velocity diversities. The system employs a 3D super-resolution approach to jointly estimate multi-path parameters like delays, Doppler shifts, and angles-of-arrival, solved through semidefinite programming. A key innovation is multi-user fusion, enhancing sensing and decoding by combining diverse user observations. This approach improves robustness and integrates multi-user perspectives for high-resolution sensing and communication. Numerical results demonstrate substantial enhancements in target estimation and communication performance. The methodology transforms user transmissions into valuable sensing opportunities, crucial for the next generation of wireless networks focused on environmental awareness and reliable data transmission. Future work includes addressing user mobility, real-time challenges, and optimizing fusion techniques in noisy conditions.",
+    "The text presents a novel pilot-free multi-user uplink framework for integrated sensing and communication (ISAC) in mm-wave networks. It enables single-antenna users to transmit without dedicated pilots, allowing the base station to decode messages and detect targets by utilizing users' spatial and velocity diversities. The system employs a 3D super-resolution approach to jointly estimate multi-path parameters like delays, Doppler shifts, and angles-of-arrival, solved through semidefinite programming. A key innovation is multi-user fusion, enhancing sensing and decoding by combining diverse user observations. This approach improves robustness and integrates multi-user perspectives for high-resolution sensing and communication. Numerical results demonstrate substantial enhancements in target estimation and communication performance. The methodology transforms user transmissions into valuable sensing opportunities, crucial for the next generation of wireless networks focused on environmental awareness and reliable data transmission. Future work includes addressing user mobility, real-time challenges, and optimizing fusion techniques in noisy conditions.",
+    'Question: what is charlotte\'s real name from henry danger, Answer: List of Henry Danger characters Charlotte[5] (Riele Downs) is one of Henry\'s best friends. She is sarcastic, clever, and smart. She is the "sass master" of the bunch, always there to snap everyone back to reality. She and Henry have been best friends for a long time and therefore she is close enough to him to tell it like it is. She is a big fan of Captain Man. In the fourth episode, Charlotte figures out Henry\'s secret and gets a job as Henry and Ray\'s manager.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 99,980 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                           | sentence_1                                                                           | label                        |
+  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                               | string                                                                               | int                          |
+  | details | <ul><li>min: 16 tokens</li><li>mean: 186.84 tokens</li><li>max: 510 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 186.84 tokens</li><li>max: 510 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | label          |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Question: who was the leader of the texas rangers, Answer: History of the Texas Ranger Division However, the Texas Rangers were not formally constituted until 1835. Austin returned to Texas after having been imprisoned in Mexico City and helped organize a council to govern the group. On October 17, at a consultation of the Provisional Government of Texas, Daniel Parker proposed a resolution to establish the Texas Rangers. He proposed creating three companies that would total some 60 men and would be known by "uniforms" consisting of a light duster (clothing) and an identification badge made from a Mexican Peso. They were instituted by Texan lawmakers on November 24. On November 28, 1835 Robert McAlpin Williamson was chosen to be the first Major of the Texas Rangers. Within two years the Rangers grew to more than 300 men.</code>                                                                                                                                                                            | <code>Question: who was the leader of the texas rangers, Answer: History of the Texas Ranger Division However, the Texas Rangers were not formally constituted until 1835. Austin returned to Texas after having been imprisoned in Mexico City and helped organize a council to govern the group. On October 17, at a consultation of the Provisional Government of Texas, Daniel Parker proposed a resolution to establish the Texas Rangers. He proposed creating three companies that would total some 60 men and would be known by "uniforms" consisting of a light duster (clothing) and an identification badge made from a Mexican Peso. They were instituted by Texan lawmakers on November 24. On November 28, 1835 Robert McAlpin Williamson was chosen to be the first Major of the Texas Rangers. Within two years the Rangers grew to more than 300 men.</code>                                                                                                                                                                            | <code>1</code> |
+  | <code>[{'from': 'human', 'value': 'Working together, Printer A and Printer B would finish a task in 40 minutes. Printer A alone would finish the task in 60 minutes. The task contains 480 pages. How many more pages per minute does Printer B print compared to Printer A?'}, {'from': 'gpt', 'value': "Let's denote the rate at which Printer A works as A pages per minute and the rate at which Printer B works as B pages per minute.\n\nWe know that Printer A alone can finish the task in 60 minutes, so:\nA * 60 = 480 pages\nA = 480 / 60\nA = 8 pages per minute\n\nWhen Printer A and Printer B work together, they can finish the task in 40 minutes, so:\n(A + B) * 40 = 480 pages\n8 + B = 480 / 40\n8 + B = 12\nB = 12 - 8\nB = 4 pages per minute\n\nNow we can find out how many more pages per minute Printer B prints compared to Printer A:\nB - A = 4 - 8\nB - A = -4\n\nThis result indicates that Printer A actually prints 4 pages per minute more than Printer B, not the other way around. If you're looking for h...</code> | <code>[{'from': 'human', 'value': 'Working together, Printer A and Printer B would finish a task in 40 minutes. Printer A alone would finish the task in 60 minutes. The task contains 480 pages. How many more pages per minute does Printer B print compared to Printer A?'}, {'from': 'gpt', 'value': "Let's denote the rate at which Printer A works as A pages per minute and the rate at which Printer B works as B pages per minute.\n\nWe know that Printer A alone can finish the task in 60 minutes, so:\nA * 60 = 480 pages\nA = 480 / 60\nA = 8 pages per minute\n\nWhen Printer A and Printer B work together, they can finish the task in 40 minutes, so:\n(A + B) * 40 = 480 pages\n8 + B = 480 / 40\n8 + B = 12\nB = 12 - 8\nB = 4 pages per minute\n\nNow we can find out how many more pages per minute Printer B prints compared to Printer A:\nB - A = 4 - 8\nB - A = -4\n\nThis result indicates that Printer A actually prints 4 pages per minute more than Printer B, not the other way around. If you're looking for h...</code> | <code>1</code> |
+  | <code>Question: where does the time zone change from central to eastern, Answer: Eastern Time Zone The boundary between time zones is set forth in the Code of Federal Regulations, with the boundary between the Eastern and Central Time Zones being specifically detailed at 49 CFR 71.[4]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>Question: where does the time zone change from central to eastern, Answer: Eastern Time Zone The boundary between time zones is set forth in the Code of Federal Regulations, with the boundary between the Eastern and Central Time Zones being specifically detailed at 49 CFR 71.[4]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>1</code> |
+* Loss: [<code>ContrastiveTensionLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastivetensionloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 20
+- `per_device_eval_batch_size`: 20
+- `num_train_epochs`: 1
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 20
+- `per_device_eval_batch_size`: 20
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.1000 | 500  | 6.2692        |
+| 0.2000 | 1000 | 6.2665        |
+| 0.3001 | 1500 | 6.2663        |
+| 0.4001 | 2000 | 6.2662        |
+| 0.5001 | 2500 | 6.266         |
+| 0.6001 | 3000 | 6.2658        |
+| 0.7001 | 3500 | 6.2657        |
+| 0.8002 | 4000 | 6.2656        |
+| 0.9002 | 4500 | 6.2656        |
+
+
+### Framework Versions
+- Python: 3.12.8
+- Sentence Transformers: 3.4.1
+- Transformers: 4.52.4
+- PyTorch: 2.6.0+cu126
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### ContrastiveTensionLoss
+```bibtex
+@inproceedings{carlsson2021semantic,
+    title={Semantic Re-tuning with Contrastive Tension},
+    author={Fredrik Carlsson and Amaru Cuba Gyllensten and Evangelia Gogoulou and Erik Ylip{"a}{"a} Hellqvist and Magnus Sahlgren},
+    booktitle={International Conference on Learning Representations},
+    year={2021},
+    url={https://openreview.net/forum?id=Ov_sMNau-PF}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.52.4",
+    "pytorch": "2.6.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7a3e9324eb4b19eb42f9eebcb8da68ff6b6b5dab6d24a389ee4836c92ebc141d
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "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

@@ -0,0 +1,63 @@
+{
+  "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
+    },
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "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]"
+}