Add new CrossEncoder model

README.md

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+---
+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: 0.4793
+      name: Map
+    - type: mrr@10
+      value: 0.4667
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5367
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3262
+      name: Map
+    - type: mrr@10
+      value: 0.5391
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3374
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.5706
+      name: Map
+    - type: mrr@10
+      value: 0.5727
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6188
+      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: 0.4587
+      name: Map
+    - type: mrr@10
+      value: 0.5262
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.4976
+      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("yjoonjang/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-plistmle-tanh")
+# Get scores for pairs of texts
+pairs = [
+    ['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
+    ['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
+    ['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (3,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'How many calories in an egg',
+    [
+        'There are on average between 55 and 80 calories in an egg depending on its size.',
+        'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
+        'Most of the calories in an egg come from the yellow yolk in the center.',
+    ]
+)
+# [{'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         | 0.4793 (-0.0103)     | 0.3262 (+0.0652)     | 0.5706 (+0.1510)     |
+| mrr@10      | 0.4667 (-0.0108)     | 0.5391 (+0.0393)     | 0.5727 (+0.1460)     |
+| **ndcg@10** | **0.5367 (-0.0038)** | **0.3374 (+0.0124)** | **0.6188 (+0.1182)** |
+
+#### 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         | 0.4587 (+0.0686)     |
+| mrr@10      | 0.5262 (+0.0582)     |
+| **ndcg@10** | **0.4976 (+0.0423)** |
+
+<!--
+## 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: 11 characters</li><li>mean: 33.21 characters</li><li>max: 83 characters</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> |
+* Samples:
+  | query                                         | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | labels                            |
+  |:----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>what is dubbin made of</code>           | <code>["A recipe for Dubbin. One of the disadvantages of living out in the sticks is that when and idea or urge pops into your head and you need to purchase something to complete it, you need to be patient. I had a bit of a browse on tinternet to find out if you could make it. turns out it is made from tallow, beeswax and fish or mink oil. Found a website where they make their own and got the recipe, tweaked it a bit and Bob's your uncle, got it made, Go Mel !!", 'I have successfully made my own Dubbin: I rendered sheep fat for the tallow (beautiful white stuff-also good for cooking and preparing cast iron cookware), and then I added Beeswax and Neatsfoot oil and a few other extras. 1 DUBBIN Can be used over any non-sealing finish, such as spirit dyes and water based dyes as well as water based inks. 2  DUBBIN allows leather to become supple without loosing its shape-it helps the leather to stay alive and always as beautiful as new.', "Dubbin is made from tallow, natural wax and OIL. The wax...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>is chromogranin a a tumor marker</code> | <code>['Chromogranin A (CGA) is a protein found in and released from neuroendocrine cells. The Chromogranin A test is used as a tumor marker. It may be ordered in combination with or in place of serotonin to help as an aid to diagnose carcinoid syndrome. Typical carcinoid symptoms include: 1  Flushing. 2  Diarrhea. 3  Abdominal pain. 4  Wheezing. 5  Valvular heart disease. ', 'Chromogranin A. Chromogranins are a family of glycoproteins with dense-core secretory vesicles that are found in neuronal and endocrine tissues. 3 Despite certain limitations, CgA is currently the most useful circulating marker for carcinoid tumors and PNET 5 and is elevated in 60% to 100% of NET. 4.', '* [email protected] Abstract: Evaluation of Serum Chromogranin A as a Useful Tumor Marker for Diagnosis of Hepatocellular Carcinoma. Journal of American Science 2011; 7(1):999-1007]. (ISSN: 1545-1003). http://www.americanscience.org. ', 'Chromogranin A (CgA) is an acidic glycoprotein expressed in the secretory gran...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>what is an MRA</code>                   | <code>['Magnetic Resonance Angiogram (MRA). Guide. A magnetic resonance angiogram (MRA) is a type of magnetic resonance imaging (MRI) scan that uses a magnetic field and pulses of radio wave energy to provide pictures of blood vessels inside the body.', 'Why It Is Done. A magnetic resonance angiogram (MRA) is done to look for: 1  A bulge (aneurysm), clot, or the buildup of fat and calcium deposits (stenosis caused by plaque) in the blood vessels leading to the brain. 2  An aneurysm or tear (dissection) in the aorta, which carries blood from the heart to the rest of the body.', 'MRA: The magnetic resonance angiogram, or MRA, is a noninvasive test that has demonstrated usefulness in defining the anatomy of blood vessels of certain size in the head and neck. MRA serves as a complement to traditional MRI scanning in evaluation of the brain and neck.', '2. MRA. An MRA is a troglodyte that writes “women’s rights --- ROFLOL!“ and “a woman is the useless skin around the vagina” on Urban Dictionary. ...</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
+  {
+      "lambda_weight": "sentence_transformers.cross_encoder.losses.ListMLELoss.ListMLELambdaWeight",
+      "activation_fct": "torch.nn.modules.activation.Tanh",
+      "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: 33.85 characters</li><li>max: 109 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 is an xc mountain bike</code>      | <code>['A cross-country mountain biker on a trail in Utah. Cross-country (XC) cycling is the most common discipline of mountain biking. Cross-country cycling became an Olympic sport in 1996 and is the only form of mountain biking practiced at the Olympics. Cross-country bicycles are some of the lightest mountain bikes, typically between 7 and 16 kilograms (15 and 35 lb). They usually feature suspension forks in front and sometimes have suspension in the rear', 'All Mountain, or Enduro bikes, are very similar to XC Trail bikes, but will have stronger frames, and a bit more travel in the suspension. Most of these bike will be in the full suspension category, and will have around 140-160 mm travel in them. The cold hard reality of modern mountain biking is that there as many bike types as there are riding disciplines. For people who are really into riding, and for whom riding is a way of life, it really isn’t uncommon for them to own 2 or 3 different types of mountain bike.', "Posts. 12. XC cou...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>when did colorado become a state</code> | <code>["Colorado Became a State August 1, 1876. The 1846-1848 war between Mexico and the United States ended in the Treaty of Guadalupe Hidalgo (1848). The impetus for the organization of the Colorado territory was the discovery of gold. On 28 February 1861, the U.S. government organized the Territory of Colorado. (Colorado City and Golden served as the territory's capital, before Denver was declared the capital in 1867.). Colorado became a state on 1 August 1876. Due to the expansion of the railroads across the plains and into the mountains, and the subsequent increase in economic linkages, the state's population quickly grew", "The impetus for the organization of the Colorado territory was the discovery of gold. On 28 February 1861, the U.S. government organized the Territory of Colorado. (Colorado City and Golden served as the territory's capital, before Denver was declared the capital in 1867.) Colorado became a state on 1 August 1876.", 'It took sixteen years, four Colorado votes, three...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>how much for roth ira value</code>      | <code>['For details, see more on Roth IRA conversions). For 2015, you can contribute the maximum $5,500 to a Roth IRA ($6,500 if you are age 50 or older by the end of the year) if you are single or the single head of a household and your modified adjusted gross income (MAGI) is less than $164,000.', 'Calculate your after tax deposit amount and how much you will save in a Roth IRA. Add this retirement calculator to your site to keep your customers coming back. This Roth IRA calculator will integrate right into your existing website automatically.', 'The current balance of your Roth IRA. Annual contribution. The amount you will contribute to your Roth IRA each year. This calculator assumes that you make your contribution at the beginning of each year. The maximum annual IRA contribution of $5,500 is unchanged for 2015. It is important to note that this is the maximum total contributed to all of your IRA accounts.', 'Contribution Limits. As of 2013 you can contribute up to $5,500 each year to y...</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
+  {
+      "lambda_weight": "sentence_transformers.cross_encoder.losses.ListMLELoss.ListMLELambdaWeight",
+      "activation_fct": "torch.nn.modules.activation.Tanh",
+      "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}
+- `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
+- `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
+- `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       | -             | -               | 0.0669 (-0.4735)         | 0.2492 (-0.0759)          | 0.0338 (-0.4669)     | 0.1166 (-0.3387)           |
+| 0.0002     | 1        | 1172.8087     | -               | -                        | -                         | -                    | -                          |
+| 0.0508     | 250      | 927.3161      | -               | -                        | -                         | -                    | -                          |
+| 0.1016     | 500      | 922.4274      | 900.2140        | 0.1417 (-0.3987)         | 0.2186 (-0.1064)          | 0.3470 (-0.1536)     | 0.2358 (-0.2196)           |
+| 0.1525     | 750      | 887.1161      | -               | -                        | -                         | -                    | -                          |
+| 0.2033     | 1000     | 883.4986      | 890.5686        | 0.3210 (-0.2194)         | 0.3210 (-0.0040)          | 0.5384 (+0.0377)     | 0.3935 (-0.0619)           |
+| 0.2541     | 1250     | 893.9945      | -               | -                        | -                         | -                    | -                          |
+| 0.3049     | 1500     | 882.1399      | 886.1149        | 0.4382 (-0.1023)         | 0.3402 (+0.0151)          | 0.6326 (+0.1319)     | 0.4703 (+0.0149)           |
+| 0.3558     | 1750     | 893.6919      | -               | -                        | -                         | -                    | -                          |
+| 0.4066     | 2000     | 860.9903      | 883.2151        | 0.4323 (-0.1081)         | 0.3507 (+0.0256)          | 0.5919 (+0.0913)     | 0.4583 (+0.0029)           |
+| 0.4574     | 2250     | 864.9159      | -               | -                        | -                         | -                    | -                          |
+| 0.5082     | 2500     | 879.1681      | 880.0072        | 0.4801 (-0.0603)         | 0.3309 (+0.0058)          | 0.5657 (+0.0651)     | 0.4589 (+0.0035)           |
+| 0.5591     | 2750     | 871.9338      | -               | -                        | -                         | -                    | -                          |
+| **0.6099** | **3000** | **876.5861**  | **876.0945**    | **0.5367 (-0.0038)**     | **0.3374 (+0.0124)**      | **0.6188 (+0.1182)** | **0.4976 (+0.0423)**       |
+| 0.6607     | 3250     | 869.3333      | -               | -                        | -                         | -                    | -                          |
+| 0.7115     | 3500     | 877.5201      | 878.9338        | 0.4719 (-0.0685)         | 0.3416 (+0.0166)          | 0.5921 (+0.0914)     | 0.4685 (+0.0132)           |
+| 0.7624     | 3750     | 874.4561      | -               | -                        | -                         | -                    | -                          |
+| 0.8132     | 4000     | 869.9234      | 875.8646        | 0.5169 (-0.0236)         | 0.3306 (+0.0055)          | 0.5979 (+0.0973)     | 0.4818 (+0.0264)           |
+| 0.8640     | 4250     | 876.4072      | -               | -                        | -                         | -                    | -                          |
+| 0.9148     | 4500     | 873.4344      | 875.6801        | 0.5085 (-0.0319)         | 0.3286 (+0.0036)          | 0.6254 (+0.1248)     | 0.4875 (+0.0322)           |
+| 0.9656     | 4750     | 858.7703      | -               | -                        | -                         | -                    | -                          |
+| -1         | -1       | -             | -               | 0.5367 (-0.0038)         | 0.3374 (+0.0124)          | 0.6188 (+0.1182)     | 0.4976 (+0.0423)           |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.5.0.dev0
+- Transformers: 4.49.0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.4.0
+- 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{lan2013position,
+    title={Position-aware ListMLE: a sequential learning process for ranking},
+    author={Lan, Yanyan and Guo, Jiafeng and Cheng, Xueqi and Liu, Tie-Yan},
+    booktitle={Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence},
+    pages={333--342},
+    year={2013}
+}
+```
+
+<!--
+## 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 @@
+{
+  "_name_or_path": "microsoft/MiniLM-L12-H384-uncased",
+  "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"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "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 sha256:2854033aefb71127095418dea3156cc6549023b48bde94c2e39abd73611e4ac9
+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]"
+}