Add new CrossEncoder model

README.md

@@ -0,0 +1,489 @@
+---
+language:
+- en
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:78704
+- loss:ListMLELoss
+base_model: microsoft/MiniLM-L12-H384-uncased
+datasets:
+- microsoft/ms_marco
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.3407
+      name: Map
+    - type: mrr@10
+      value: 0.3234
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3834
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.2803
+      name: Map
+    - type: mrr@10
+      value: 0.3818
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.252
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.2448
+      name: Map
+    - type: mrr@10
+      value: 0.2333
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.2953
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-nano-beir
+      name: Cross Encoder Nano BEIR
+    dataset:
+      name: NanoBEIR R100 mean
+      type: NanoBEIR_R100_mean
+    metrics:
+    - type: map
+      value: 0.2886
+      name: Map
+    - type: mrr@10
+      value: 0.3128
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3103
+      name: Ndcg@10
+---
+
+# CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) <!-- at revision 44acabbec0ef496f6dbc93adadea57f376b7c0ec -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+- **Training Dataset:**
+    - [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("Wi-Fi/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-listmle")
+# Get scores for pairs of texts
+pairs = [
+    ['what schooling is required to become a nephrologist', 'Educational Requirements. Necessary education to become a nephrologist includes a medical doctoral degree, completion of an Accreditation Council for Graduate Medical Education (ACGME) internal medicine residency program and at least two years of professional experience working with patients. '],
+    ['what schooling is required to become a nephrologist', 'How to Become a Nephrologist. Nephrology is a discipline of internal medicine that focuses on the kidney and related conditions and diseases. Nephrologists are specially trained medical doctors that are experts on treating patients with conditions related to the kidneys and other related areas. Nephrologists diagnose and treat a variety of conditions such as kidney disease, electrolyte disorders, high blood pressure, and kidney stones. They perform many different types of tests to assist in diagnosis and treatments such as blood tests, urine tests, biopsies, ultrasounds, and placement of catheters.'],
+    ['what schooling is required to become a nephrologist', '10. While in your residency period, try to make contacts with other nephrologists. Having good contacts in the field of nephrology, as well as urology, is a very good step while in your residency and if you are sure of becoming a nephrologist. Since nephrology is a branch of internal medicine, you will have to do your residency in internal medicine first. During your residency period, you will work in a hospital. You are responsible for patients, and supervised by senior residents as well as professional doctors'],
+    ['what schooling is required to become a nephrologist', "Residency and Fellowships. Graduation from medical school is just the beginning of the nephrologist's medical education. After graduation, newly minted doctors do their medical residency at a hospital. Would-be nephrologists complete an internal medicine residency, which lasts for three years. Medical School. Budding nephrologists must graduate from a medical school. The road just to get into medical school after graduation from an accredited four-year college or university is arduous."],
+    ['what schooling is required to become a nephrologist', 'Formal education and training requirements for physicians are among the most demanding of any occupation-4 years of undergraduate school, 4 years of medical school, and 3 to 8 years of internship and residency, depending on the specialty selected. Physicians specializing in anesthesiology must spend a substantial number of years completing education and training requirements, including 4 years of undergraduate school, 4 … years of medical school, and 3 to 8 years of residency.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'what schooling is required to become a nephrologist',
+    [
+        'Educational Requirements. Necessary education to become a nephrologist includes a medical doctoral degree, completion of an Accreditation Council for Graduate Medical Education (ACGME) internal medicine residency program and at least two years of professional experience working with patients. ',
+        'How to Become a Nephrologist. Nephrology is a discipline of internal medicine that focuses on the kidney and related conditions and diseases. Nephrologists are specially trained medical doctors that are experts on treating patients with conditions related to the kidneys and other related areas. Nephrologists diagnose and treat a variety of conditions such as kidney disease, electrolyte disorders, high blood pressure, and kidney stones. They perform many different types of tests to assist in diagnosis and treatments such as blood tests, urine tests, biopsies, ultrasounds, and placement of catheters.',
+        '10. While in your residency period, try to make contacts with other nephrologists. Having good contacts in the field of nephrology, as well as urology, is a very good step while in your residency and if you are sure of becoming a nephrologist. Since nephrology is a branch of internal medicine, you will have to do your residency in internal medicine first. During your residency period, you will work in a hospital. You are responsible for patients, and supervised by senior residents as well as professional doctors',
+        "Residency and Fellowships. Graduation from medical school is just the beginning of the nephrologist's medical education. After graduation, newly minted doctors do their medical residency at a hospital. Would-be nephrologists complete an internal medicine residency, which lasts for three years. Medical School. Budding nephrologists must graduate from a medical school. The road just to get into medical school after graduation from an accredited four-year college or university is arduous.",
+        'Formal education and training requirements for physicians are among the most demanding of any occupation-4 years of undergraduate school, 4 years of medical school, and 3 to 8 years of internship and residency, depending on the specialty selected. Physicians specializing in anesthesiology must spend a substantial number of years completing education and training requirements, including 4 years of undergraduate school, 4 … years of medical school, and 3 to 8 years of residency.',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Cross Encoder Reranking
+
+* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | NanoMSMARCO_R100     | NanoNFCorpus_R100    | NanoNQ_R100          |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.3407 (-0.1489)     | 0.2803 (+0.0193)     | 0.2448 (-0.1748)     |
+| mrr@10      | 0.3234 (-0.1541)     | 0.3818 (-0.1180)     | 0.2333 (-0.1934)     |
+| **ndcg@10** | **0.3834 (-0.1570)** | **0.2520 (-0.0730)** | **0.2953 (-0.2053)** |
+
+#### Cross Encoder Nano BEIR
+
+* Dataset: `NanoBEIR_R100_mean`
+* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "rerank_k": 100,
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.2886 (-0.1015)     |
+| mrr@10      | 0.3128 (-0.1552)     |
+| **ndcg@10** | **0.3103 (-0.1451)** |
+
+<!--
+## 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
+
+#### ms_marco
+
+* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
+* Size: 78,704 training samples
+* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                          | docs                                                                                   | labels                                                                                 |
+  |:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
+  | type    | string                                                                                         | list                                                                                   | list                                                                                   |
+  | details | <ul><li>min: 10 characters</li><li>mean: 34.09 characters</li><li>max: 99 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> |
+* Samples:
+  | query                                                           | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | labels                            |
+  |:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>'average cost of hospital stay per day for sepsis'</code> | <code>['1 The incremental costs incurred by patients on their last full day of hospital stay were $420 per day on average, or just 2.4 percent of the $17,734 average total cost. 2  End-of-stay costs represented only a slightly higher percentage of total costs when the length of stay was as short as four days.', 'Cost per stay and cost per day for hospitalizations with a secondary diagnosis of septicemia also. increased between 2000 and 2009 (from $30,900 to $33,900; from $2,100 to $2,300), although the. average length of these stays remained relatively stable.', 'They found: 1  The incremental costs incurred by patients on their last full day of hospital stay were $420 per day on average, or just 2.4 percent of the $17,734 average total cost. 2  End-of-stay costs represented only a slightly higher percentage of total costs when the length of stay was as short as four days.', '60 percent of hospital stays in 2011. Forty-seven percent of aggregate costs were billed to Medicare, and. the mean c...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>what is lion tailing of trees</code>                      | <code>["Lion's tail pruning is a method of pruning the lower branches from tree limbs, leaving a top tuft of foliage that resembles a lion's tail. Previously advocated by some arborists as a way of increasing a tree's wind resistance, the practice has become controversial. ", 'Lions-tailed. This over-lifting or over-thinning is often referred to as lions-tailing. It leaves live branches only at the tips of the canopy. Tremendous numbers of sprouts often result from this type of tree mutilation.', 'To the untrained eye, a lion-tailed tree may appear to be well groomed. Many tree trimmers who routinely lion-tail trees are unaware of the problems that they cause. Lion-tailing is harmful to trees and it increases susceptibility to wind-failure. Clients may ask for this type of pruning because they’re unaware that the practice is detrimental to their trees. Many of these trees are hundreds of years old and can be seen on private, city, county and community properties. It is a practice of trimming...</code> | <code>[1, 1, 0, 0, 0, ...]</code> |
+  | <code>where is kuwait on the world map</code>                   | <code>["Kuwait is located in the Middle East. Kuwait is bordered by the Persian Gulf to the east, Iraq to the west and north, and Saudi Arabia to the south. Iraq, Saudi Arabia. Abdali, Al Ahmadi, Al Jahra, Al Khiran, Al Kuwayt (Kuwait), Al Wafrah, As Subayhiyah, Ash Shuwayhk, Az Zawr, Bubiyan, Mardaz Hudud al Abdali, Mina' 'Abd Allah and Qasr as Sabiyah.", 'Iraq. Iraq is located in the Middle East. It is bound by Iran to the east, Turkey to the north, Syria to the northwest, Jordan to the west, Saudi Arabia to the southwest and south as well as Kuwait and the Persian Gulf to the southeast....', 'Map of Kuwait: Map of the World Map of Asia Map of Kuwait. Map-Kuwait. Google-Kuwait. Earth-Kuwait. Weather-Kuwait. World Map Finder, Map of Kuwait. The best web resource for', 'The State of Kuwait is a sovereign emirate on the coast of the Persian Gulf, enclosed by Saudi Arabia to the south and Iraq to the north and west. The name is a diminutive of an Arabic word meaning fortress built near water.....</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+* Loss: [<code>ListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listmleloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "mini_batch_size": 16,
+      "respect_input_order": true
+  }
+  ```
+
+### Evaluation Dataset
+
+#### ms_marco
+
+* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
+* Size: 1,000 evaluation samples
+* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                         | docs                                                                                   | labels                                                                                 |
+  |:--------|:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
+  | type    | string                                                                                        | list                                                                                   | list                                                                                   |
+  | details | <ul><li>min: 11 characters</li><li>mean: 34.4 characters</li><li>max: 97 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> |
+* Samples:
+  | query                                                            | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | labels                            |
+  |:-----------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>what schooling is required to become a nephrologist</code> | <code>['Educational Requirements. Necessary education to become a nephrologist includes a medical doctoral degree, completion of an Accreditation Council for Graduate Medical Education (ACGME) internal medicine residency program and at least two years of professional experience working with patients. ', 'How to Become a Nephrologist. Nephrology is a discipline of internal medicine that focuses on the kidney and related conditions and diseases. Nephrologists are specially trained medical doctors that are experts on treating patients with conditions related to the kidneys and other related areas. Nephrologists diagnose and treat a variety of conditions such as kidney disease, electrolyte disorders, high blood pressure, and kidney stones. They perform many different types of tests to assist in diagnosis and treatments such as blood tests, urine tests, biopsies, ultrasounds, and placement of catheters.', '10. While in your residency period, try to make contacts with other nephrologists. Having g...</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>shelled peanut/ carb or protein</code>                     | <code>['One serving of shelled peanuts provides 6 g of carbohydrates and also provides 7 g of protein. Salted shelled peanuts contain 230 mg of sodium, 187 mg of potassium, but they do not provide vitamin A, vitamin C, calcium or iron. Unsalted shelled peanuts contain no sodium, calcium or iron, but they do provide 180 mg of potassium. Both salted and unsalted shelled peanuts provide 2 g of dietary fiber.', 'Carbs in Peanuts. The favorite choice for the term Peanuts is 1 ounce of Peanuts which has about 5 grams of carbohydrate. The total carbohyrate, sugar, fiber and estimated net carbs (non-fiber carbs) for a variety of types and serving sizes of Peanuts is shown below. View other nutritional values (such as Calories or Fats) using the filter below: Calories | Total Carbs | Total Fats | Protein | Sodium | Cholesterol | Vitamins.', 'A 1-ounce serving of peanuts provides 7g of protein, which can meet up to 15 percent of your daily protein needs. Fiber, which accounts for over 1/3 of the carbohydrates in peanuts, is beneficial to the digestive system and slows the absorption of sugar and fat, helping your body regulate blood sugar levels.', 'Protein, fats, and fiber are the major components that make up peanuts. The good news is that these major components are all the healthy types when it comes to peanuts. The protein is plant-based; the fat is unsaturated, and the fiber is the main type of complex carbohydrate in peanuts.', 'When you think of healthy fats, you can think of peanuts, peanut butter, and peanut oil. That is because at least half of the fat in peanuts is heart-healthy monounsaturated fat, the kind found in olive oil and avocados. And over 30% is polyunsaturated fat; another good fat important in a healthy diet.']</code> | <code>[1, 0, 0, 0, 0]</code>      |
+  | <code>terahertz radiation definition</code>                      | <code>['SecurActive Performance Vision. The terahertz, abbreviated THz, is a unit of electromagnetic (EM) wave frequency equal to one trillion hertz (10 12 Hz). The terahertz is used as an indicator of the frequency of infrared (IR), visible, and ultraviolet (UV) radiation. Download this Pocket Guide to Network Management and Monitoring. The terahertz is not commonly used in computer and wireless technology, although it is possible that a microprocessor with a clock speed of 1 THz might someday be developed.', 'Electromagnetic radiation with waves of frequencies ranging from 0.3 × 10 12 to 3 × 10 12 hertz and of wavelengths ranging from 0.1 to 1 millimeter of potential use in detection, imaging, and communications technologies. Also called submillimeter radiation. Radiation from airport body scanner may detect early signs of skin ... by Asian News International. is non-ionizing, and reliable studies have shown that active operation in this frequency band is harmless to humans.', 'How to cite...</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>[1, 0, 0, 0, 0, ...]</code> |
+* Loss: [<code>ListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listmleloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "mini_batch_size": 16,
+      "respect_input_order": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `bf16`: True
+- `load_best_model_at_end`: True
+
+#### 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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: 12
+- `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`: 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`: True
+- `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}
+- `tp_size`: 0
+- `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`: proportional
+
+</details>
+
+### Training Logs
+| Epoch     | Step     | Training Loss | Validation Loss | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10  | NanoBEIR_R100_mean_ndcg@10 |
+|:---------:|:--------:|:-------------:|:---------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
+| -1        | -1       | -             | -               | 0.0300 (-0.5104)         | 0.2528 (-0.0723)          | 0.0168 (-0.4839)     | 0.0999 (-0.3555)           |
+| 0.0008    | 1        | 14.1367       | -               | -                        | -                         | -                    | -                          |
+| 0.2033    | 250      | 12.2769       | -               | -                        | -                         | -                    | -                          |
+| 0.4065    | 500      | 11.248        | 11.2104         | 0.1129 (-0.4275)         | 0.2002 (-0.1248)          | 0.1527 (-0.3480)     | 0.1553 (-0.3001)           |
+| 0.6098    | 750      | 11.2506       | -               | -                        | -                         | -                    | -                          |
+| **0.813** | **1000** | **11.1721**   | **11.1729**     | **0.3834 (-0.1570)**     | **0.2520 (-0.0730)**      | **0.2953 (-0.2053)** | **0.3103 (-0.1451)**       |
+| -1        | -1       | -             | -               | 0.3834 (-0.1570)         | 0.2520 (-0.0730)          | 0.2953 (-0.2053)     | 0.3103 (-0.1451)           |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+#### ListMLELoss
+```bibtex
+@inproceedings{10.1145/1390156.1390306,
+    title = {Listwise Approach to Learning to Rank - Theory and Algorithm},
+    author = {Xia, Fen and Liu, Tie-Yan and Wang, Jue and Zhang, Wensheng and Li, Hang},
+    booktitle = {Proceedings of the 25th International Conference on Machine Learning},
+    pages = {1192-1199},
+    year = {2008},
+    url = {https://doi.org/10.1145/1390156.1390306},
+}
+```
+
+<!--
+## 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 +1,34 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "4.1.0"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f2b18f2e2a7552bf2b4915f056a274f140bc62c8a1ca22f68fdaf35470de2d7
+size 133464836

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "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
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}