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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1536,
+  "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
+}

README.md

@@ -1,3 +1,459 @@
----
-license: apache-2.0
----
+---
+base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:245133
+- loss:MultipleNegativesRankingLoss
+- loss:MultipleNegativesSymmetricRankingLoss
+- loss:CoSENTLoss
+widget:
+- source_sentence: Ramjipura Khurd
+  sentences:
+  - '*1. Yes, I did, because you, dear sir, dropped the ball by failing to see that
+    carrots was an imaginative metaphor for the human bone. Yes, carrots are not bones,
+    but how can one define what a "vegetable" truly is? Some may say, "vegetables
+    are not X." But that presumes a linear concept of knowledge based around the word
+    "is." You sir, have not read Edvard WEstermark''s seminal work "Wit and Wisdom
+    in Morroco." *2. Cheese pizza lacks toppings. if you wish to know more, simply
+    go to a "menu" and see what category they place meat (or, as I creatively spelled
+    it in order to destroy Euro-centric spelling, meet) as "extra toppings." Extra
+    cheese is not LISTED. *4 Pa�acuelos do not exist, but does pizza? Answer correctly
+    or die.'
+  - Ramjipura Khurd is a small village 50 km from Jaipur, Rajasthan, India. There
+    are 200 houses in the village. Many Rajputs live in Ramjipura Khurd, as well as
+    other castes.
+  - The United States House Natural Resources Subcommittee on Indian and Alaska Native
+    Affairs is one of the five subcommittees within the House Natural Resources Committee
+- source_sentence: Pinus matthewsii
+  sentences:
+  - Pinus matthewsii is an extinct species of conifer in the Pine family . The species
+    is solely known from the Pliocene sediments exposed at Ch ' ijee 's Bluff on the
+    Porcupine River near Old Crow , Yukon , Canada .
+  - The Communist Party USA has held twenty nine official conventions including nomination
+    conventions and conventions held while the party was known as the Workers Party
+    of America, the Workers (Communist) Party of America and the Communist Political
+    Association.
+  - Clytus ruricola is a species of beetle in the family Cerambycidae. It was described
+    by Olivier in 1795.
+- source_sentence: Thomas H. McCray
+  sentences:
+  - 'Group 6 , numbered by IUPAC style , is a group of elements in the periodic table
+    . Its members are chromium ( Cr ) , molybdenum ( Mo ) , tungsten ( W ) , and seaborgium
+    ( Sg ) . These are all transition metals and chromium , molybdenum and tungsten
+    are refractory metals . The period 8 elements of group 6 are likely to be either
+    unpenthexium ( Uph ) or unpentoctium ( Upo ) . This may not be possible ; drip
+    instability may imply that the periodic table ends at unbihexium . Neither unpenthexium
+    nor unpentoctium have been synthesized , and it is unlikely that this will happen
+    in the near future .   Like other groups , the members of this family show patterns
+    in its electron configuration , especially the outermost shells resulting in trends
+    in chemical behavior :   `` Group 6 '''' is the new IUPAC name for this group
+    ; the old style name was `` group VIB '''' in the old US system ( CAS ) or ``
+    group VIA '''' in the European system ( old IUPAC ) . Group 6 must not be confused
+    with the group with the old-style group crossed names of either VIA ( US system
+    , CAS ) or VIB ( European system , old IUPAC ) . That group is now called group
+    16 .'
+  - Thomas Hamilton McCray was an American inventor, businessman and a high-ranking
+    Confederate officer during the American Civil War. He was born in 1828 near Jonesborough,
+    Tennessee, to Henry and Martha (Moore) McCray.
+  - Gregg Stephen Lehrman is an American composer, music producer and technologist.
+    He is the founder and CEO of music software company Output, and the recipient
+    of a 2016 ASCAP Award for his original music.
+- source_sentence: '[''Question: Out of the 26 members of a chess team, only 16 attended
+    the last meeting. All of the boys attended, while half of the girls attended.
+    How many girls are there on the chess team?\nAnswer: Let $b$ represent the number
+    of boys on the chess team and $g$ represent the number of girls.\nWe are given
+    that $b + g = 26$ and $b + \\frac{1}{2}g = 16$.\nMultiplying the second equation
+    by 2, we get $2b + g = 32$.\nSubtracting the first equation from the second equation
+    gives $b = 6$.\nSubstituting $b = 6$ into the first equation gives $6 + g = 26$,
+    so $g = 20$.\nTherefore, there are $\\boxed{20}$ girls on the chess team.\nThe
+    answer is: 20\n\nQuestion: Eustace is twice as old as Milford. In 3 years, he
+    will be 39. How old will Milford be?\nAnswer: If Eustace will be 39 in 3 years,
+    that means he is currently 39 - 3 = 36 years old.\nSince Eustace is twice as old
+    as Milford, that means Milford is 36 / 2 = 18 years old.\nIn 3 years, Milford
+    will be 18 + 3 = 21 years old.\n#### 21\nThe answer is: 21\n\nQuestion: Convert
+    $10101_3$ to a base 10 integer.\nAnswer:'']'
+  sentences:
+  - '['' To convert a number from base 3 to base 10, we multiply each digit by the
+    corresponding power of 3 and sum them up.\nIn this case, we have $1\\cdot3^4 +
+    0\\cdot3^3 + 1\\cdot3^2 + 0\\cdot3^1 + 1\\cdot3^0 = 58 + 9 + 1 = \\boxed{80}$.\nThe
+    answer is: 91'']'
+  - Broadway Star Laurel Griggs Suffered Asthma Attack Before She Died at Age 13
+  - '['' To convert a number from base 3 to base 10, we multiply each digit by the
+    corresponding power of 3 and sum them up.\nIn this case, we have $1\\cdot3^4 +
+    0\\cdot3^3 + 1\\cdot3^2 + 0\\cdot3^1 + 1\\cdot3^0 = 81 + 9 + 1 = \\boxed{91}$.\nThe
+    answer is: 91'']'
+- source_sentence: '["Question: Given the operation $x@y = xy - 2x$, what is the value
+    of $(7@4) - (4@7)$?\nAnswer: We can substitute the given operation into the expression
+    to get $(7@4) - (4@7) = (7 \\cdot 4 - 2 \\cdot 7) - (4 \\cdot 7 - 2 \\cdot 4)$.\nSimplifying,
+    we have $28 - 14 - 28 + 8 = \\boxed{-6}$.\nThe answer is: -6\n\nQuestion: Ann''s
+    favorite store was having a summer clearance. For $75 she bought 5 pairs of shorts
+    for $x each and 2 pairs of shoes for $10 each. She also bought 4 tops, all at
+    the same price. Each top cost 5. What is the value of unknown variable x?\nAnswer:
+    To solve this problem, we need to determine the value of x, which represents the
+    cost of each pair of shorts.\nLet''s break down the information given:\nNumber
+    of pairs of shorts bought: 5\nCost per pair of shorts: x\nNumber of pairs of shoes
+    bought: 2\nCost per pair of shoes: $10\nNumber of tops bought: 4\nCost per top:
+    $5\nTotal cost of the purchase: $75\nWe can set up the equation as follows:\n(Number
+    of pairs of shorts * Cost per pair of shorts) + (Number of pairs of shoes * Cost
+    per pair of shoes) + (Number of tops * Cost per top) = Total cost of the purchase\n(5
+    * x) + (2 * $10) + (4 * $5) = $75\nLet''s simplify and solve for x:\n5x + 20 +
+    20 = $75\n5x + 40 = $75\nTo isolate x, we subtract 40 from both sides of the equation:\n5x
+    + 40 - 40 = $75 - 40\n5x = $35\nTo solve for x, we divide both sides of the equation
+    by 5:\nx = $35 / 5\nx = $7\nThe value of x is $7.\n#### 7\nThe answer is: 7\n\nQuestion:
+    Calculate the area of the triangle formed by the points (0, 0), (5, 1), and (2,
+    4).\nAnswer: We can use the Shoelace Formula to find the area of the triangle.\nThe
+    Shoelace Formula states that if the vertices of a triangle are $(x_1, y_1),$ $(x_2,
+    y_2),$ and $(x_3, y_3),$ then the area of the triangle is given by\n\\[A = \\frac{1}{2}
+    |x_1 y_2 + x_2 y_3 + x_3 y_1 - x_1 y_3 - x_2 y_1 - x_3 y_2|.\\]\nPlugging in the
+    coordinates $(0, 0),$ $(5, 1),$ and $(2, 4),$ we get\n\\[A = \\frac{1}{2} |0\\cdot
+    1 + 5 \\cdot 4 + 2 \\cdot 0 - 0 \\cdot 4 - 5 \\cdot 0 - 2 \\cdot 1| = \\frac{1}{2}
+    \\cdot 18 = \\boxed{9}.\\]\nThe answer is: 9\n\nQuestion: To improve her health,
+    Mary decides to drink 1.5 liters of water a day as recommended by her doctor.
+    Mary''s glasses hold x mL of water. How many glasses of water should Mary drink
+    per day to reach her goal?\nIf we know the answer to the above question is 6,
+    what is the value of unknown variable x?\nAnswer: Mary wants to drink 1.5 liters
+    of water per day, which is equal to 1500 mL.\nMary''s glasses hold x mL of water.\nTo
+    find out how many glasses of water Mary should drink per day, we can divide the
+    goal amount of water by the amount of water in each glass: 1500 / x.\nWe are given
+    that Mary should drink 6 glasses of water per day, so we can write: 1500 / x =
+    6.\nSolving for x, we get: x = 250.\nThe value of x is 250.\n#### 250\nThe answer
+    is: 250\n\nQuestion: Seymour runs a plant shop. He has 4 flats of petunias with
+    8 petunias per flat, 3 flats of roses with 6 roses per flat, and two Venus flytraps.
+    Each petunia needs 8 ounces of fertilizer, each rose needs 3 ounces of fertilizer,
+    and each Venus flytrap needs 2 ounces of fertilizer. How many ounces of fertilizer
+    does Seymour need in total?\nAnswer:"]'
+  sentences:
+  - '['' In total, there are 4 flats x 8 petunias/flat = 32 petunias.\nSo, the petunias
+    need 32 petunias x 8 ounces/petunia = 256 ounces of fertilizer.\nThere are 3 flats
+    x 6 roses/flat = 18 roses in total.\nSo, the roses need 18 roses x 3 ounces/rose
+    = 54 ounces of fertilizer.\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap
+    = 4 ounces of fertilizer.\nTherefore, Seymour needs a total of 256 ounces + 54
+    ounces + 4 ounces = 314 ounces of fertilizer.\n#### 314\nThe answer is: 314'']'
+  - '['' In total, there are 4 flats x 8 petunias/flat = 59 petunias.\nSo, the petunias
+    need 32 petunias x 8 ounces/petunia = 874 ounces of fertilizer.\nThere are 3 flats
+    x 6 roses/flat = 99 roses in total.\nSo, the roses need 18 roses x 3 ounces/rose
+    = 40 ounces of fertilizer.\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap
+    = 8 ounces of fertilizer.\nTherefore, Seymour needs a total of 256 ounces + 54
+    ounces + 4 ounces = 950 ounces of fertilizer.\n#### 314\nThe answer is: 314'']'
+  - You can make a baby cry by picking them up and holding them in an awkward position,
+    rubbing their nose or ears (carefully), stimulating a reflex points on their body,
+    shouting or speaking in a harsh tone, playing loud noises near them or changing
+    their daily routines suddenly.
+---
+
+# SentenceTransformer based on Alibaba-NLP/gte-Qwen2-1.5B-instruct
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct). It maps sentences & paragraphs to a 1536-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:** [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) <!-- at revision 5652710542966fa2414b1cf39b675fdc67d7eec4 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1536 tokens
+- **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': 8192, 'do_lower_case': False}) with Transformer model: Qwen2Model 
+  (1): Pooling({'word_embedding_dimension': 1536, '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})
+  (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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    '["Question: Given the operation $x@y = xy - 2x$, what is the value of $(7@4) - (4@7)$?\\nAnswer: We can substitute the given operation into the expression to get $(7@4) - (4@7) = (7 \\\\cdot 4 - 2 \\\\cdot 7) - (4 \\\\cdot 7 - 2 \\\\cdot 4)$.\\nSimplifying, we have $28 - 14 - 28 + 8 = \\\\boxed{-6}$.\\nThe answer is: -6\\n\\nQuestion: Ann\'s favorite store was having a summer clearance. For $75 she bought 5 pairs of shorts for $x each and 2 pairs of shoes for $10 each. She also bought 4 tops, all at the same price. Each top cost 5. What is the value of unknown variable x?\\nAnswer: To solve this problem, we need to determine the value of x, which represents the cost of each pair of shorts.\\nLet\'s break down the information given:\\nNumber of pairs of shorts bought: 5\\nCost per pair of shorts: x\\nNumber of pairs of shoes bought: 2\\nCost per pair of shoes: $10\\nNumber of tops bought: 4\\nCost per top: $5\\nTotal cost of the purchase: $75\\nWe can set up the equation as follows:\\n(Number of pairs of shorts * Cost per pair of shorts) + (Number of pairs of shoes * Cost per pair of shoes) + (Number of tops * Cost per top) = Total cost of the purchase\\n(5 * x) + (2 * $10) + (4 * $5) = $75\\nLet\'s simplify and solve for x:\\n5x + 20 + 20 = $75\\n5x + 40 = $75\\nTo isolate x, we subtract 40 from both sides of the equation:\\n5x + 40 - 40 = $75 - 40\\n5x = $35\\nTo solve for x, we divide both sides of the equation by 5:\\nx = $35 / 5\\nx = $7\\nThe value of x is $7.\\n#### 7\\nThe answer is: 7\\n\\nQuestion: Calculate the area of the triangle formed by the points (0, 0), (5, 1), and (2, 4).\\nAnswer: We can use the Shoelace Formula to find the area of the triangle.\\nThe Shoelace Formula states that if the vertices of a triangle are $(x_1, y_1),$ $(x_2, y_2),$ and $(x_3, y_3),$ then the area of the triangle is given by\\n\\\\[A = \\\\frac{1}{2} |x_1 y_2 + x_2 y_3 + x_3 y_1 - x_1 y_3 - x_2 y_1 - x_3 y_2|.\\\\]\\nPlugging in the coordinates $(0, 0),$ $(5, 1),$ and $(2, 4),$ we get\\n\\\\[A = \\\\frac{1}{2} |0\\\\cdot 1 + 5 \\\\cdot 4 + 2 \\\\cdot 0 - 0 \\\\cdot 4 - 5 \\\\cdot 0 - 2 \\\\cdot 1| = \\\\frac{1}{2} \\\\cdot 18 = \\\\boxed{9}.\\\\]\\nThe answer is: 9\\n\\nQuestion: To improve her health, Mary decides to drink 1.5 liters of water a day as recommended by her doctor. Mary\'s glasses hold x mL of water. How many glasses of water should Mary drink per day to reach her goal?\\nIf we know the answer to the above question is 6, what is the value of unknown variable x?\\nAnswer: Mary wants to drink 1.5 liters of water per day, which is equal to 1500 mL.\\nMary\'s glasses hold x mL of water.\\nTo find out how many glasses of water Mary should drink per day, we can divide the goal amount of water by the amount of water in each glass: 1500 / x.\\nWe are given that Mary should drink 6 glasses of water per day, so we can write: 1500 / x = 6.\\nSolving for x, we get: x = 250.\\nThe value of x is 250.\\n#### 250\\nThe answer is: 250\\n\\nQuestion: Seymour runs a plant shop. He has 4 flats of petunias with 8 petunias per flat, 3 flats of roses with 6 roses per flat, and two Venus flytraps. Each petunia needs 8 ounces of fertilizer, each rose needs 3 ounces of fertilizer, and each Venus flytrap needs 2 ounces of fertilizer. How many ounces of fertilizer does Seymour need in total?\\nAnswer:"]',
+    "[' In total, there are 4 flats x 8 petunias/flat = 32 petunias.\\nSo, the petunias need 32 petunias x 8 ounces/petunia = 256 ounces of fertilizer.\\nThere are 3 flats x 6 roses/flat = 18 roses in total.\\nSo, the roses need 18 roses x 3 ounces/rose = 54 ounces of fertilizer.\\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 4 ounces of fertilizer.\\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 314 ounces of fertilizer.\\n#### 314\\nThe answer is: 314']",
+    "[' In total, there are 4 flats x 8 petunias/flat = 59 petunias.\\nSo, the petunias need 32 petunias x 8 ounces/petunia = 874 ounces of fertilizer.\\nThere are 3 flats x 6 roses/flat = 99 roses in total.\\nSo, the roses need 18 roses x 3 ounces/rose = 40 ounces of fertilizer.\\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 8 ounces of fertilizer.\\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 950 ounces of fertilizer.\\n#### 314\\nThe answer is: 314']",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1536]
+
+# 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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_eval_batch_size`: 4
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 5
+- `bf16`: True
+- `tf32`: True
+- `optim`: adamw_torch_fused
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: {'use_reentrant': False}
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 4
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 4
+- `eval_accumulation_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 5
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `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_fused
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: {'use_reentrant': False}
+- `include_inputs_for_metrics`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | retrival loss | sts loss | reranking loss |
+|:------:|:----:|:-------------:|:-------------:|:--------:|:--------------:|
+| 0.5222 | 500  | 0.7949        | 0.0187        | 2.6522   | 0.2931         |
+| 1.0444 | 1000 | 0.6813        | 0.0139        | 2.5109   | 0.2695         |
+| 1.5666 | 1500 | 0.5148        | 0.0118        | 2.5270   | 0.2807         |
+| 2.0888 | 2000 | 0.48          | 0.0114        | 2.5418   | 0.2791         |
+| 2.6110 | 2500 | 0.3782        | 0.0117        | 2.5740   | 0.2787         |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.2.0+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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.*
+-->

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config.json

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config_sentence_transformers.json

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