Add new CrossEncoder model

README.md

@@ -0,0 +1,458 @@
+---
+language:
+- en
+tags:
+- sentence-transformers
+- cross-encoder
+- text-classification
+- generated_from_trainer
+- dataset_size:78704
+- loss:ListNetLoss
+base_model: microsoft/MiniLM-L12-H384-uncased
+datasets:
+- microsoft/ms_marco
+pipeline_tag: text-classification
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+co2_eq_emissions:
+  emissions: 206.93024560685208
+  energy_consumed: 0.532362183901426
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 1.708
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
+  results: []
+---
+
+# 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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## 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("tomaarsen/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-listnet-sigmoid")
+# 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`, `NanoNFCorpus` and `NanoNQ`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator)
+
+| Metric      | NanoMSMARCO          | NanoNFCorpus         | NanoNQ               |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.5282 (+0.0387)     | 0.3272 (+0.0662)     | 0.5598 (+0.1402)     |
+| mrr@10      | 0.5156 (+0.0381)     | 0.5520 (+0.0521)     | 0.5627 (+0.1360)     |
+| **ndcg@10** | **0.5787 (+0.0383)** | **0.3588 (+0.0337)** | **0.6221 (+0.1215)** |
+
+#### 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)
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.4717 (+0.0817)     |
+| mrr@10      | 0.5434 (+0.0754)     |
+| **ndcg@10** | **0.5199 (+0.0645)** |
+
+<!--
+## 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: 33.13 characters</li><li>max: 90 characters</li></ul> | <ul><li>size: 10 elements</li></ul> | <ul><li>size: 10 elements</li></ul> |
+* Samples:
+  | query                                                | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | labels                            |
+  |:-----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>how much does the average funeral cost</code>  | <code>['Average Funeral Costs. According to the Federal Trade Commission, the average funeral costs in the United States can be well over $10,000 by the time you add floral arrangements, prayer cards and family transportation. Traditionally, when people think of funeral expenses they think of things like a casket and flowers. Headstones often start around $500 and run upwards of $4000. The materials used for construction contribute to a wide price range. An average granite headstone in 2009 cost about $1500. If your loved one did not already have a cemetery plot, you will need to purchase one. Prices of cemetery plots depend on location. They start as low as a few hundred dollars and can be upward of a few thousand', 'National average funeral cost: $5,000-$15,000 CAD. In Canada, the price of a funeral varies greatly by area and method. A basic cremation service in Toronto can cost $1,470 CAD to cut costs. Many families are opting to choose at-home funerals. National average funeral cost: $6,...</code> | <code>[1, 1, 0, 0, 0, ...]</code> |
+  | <code>what is the seven sisters constellation</code> | <code>["The Seven Sisters is a small grouping of stars better known as the Pleiades, in the constellation Taurus. It is a group of six to seven stars (with the naked eye) and about 36 … stars (with binoculars) easily viewable on any clear night in the wintertime or very early spring. The Seven Sisters is what is known as an open cluster of stars. It's also known as The Pleiades. (PLEE-uh-DEES). 3 people found this useful. Edit. Share to: 1  The Periodic Table of Elements Life is sustained by a number of chemical elements.", 'For other uses of Pleiades or Pleiades, pléiades See (pleiades) . Disambiguation in, astronomy The (/pleiades.ˈplaɪ/ ədiːz /or.ˈpliː/), ədiːz Or Seven (Sisters messier 45 Or), m45 is an open star cluster containing-middle aged Hot-b type stars located in the constellation Of. taurus The nine brightest stars of the Pleiades are named for the Seven Sisters of Greek mythology: Sterope, Merope, Electra, Maia, Taygeta, Celaeno, and Alcyone, along with their parents Atlas and ...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>the name nicole means</code>                   | <code>["Nicole is a feminine given name and a surname. The given name Nicole is of Greek origin and means victorious people. It's evolved into a French feminine derivative of the masculine given name Nicolas. There are many variants. The surname Nicole originates in Netherlands where it was notable for its various branches, and associated status or influenc", "Nicole is a feminine given name and a surname.The given name Nicole is of Greek origin and means victorious people. It's evolved into a French feminine derivative of the masculine given name Nicholas.The given name Nicole is of Greek origin and means v. A feminine form of Nicolas, which is from the Greek Nikolaos, a compound name composed of the elements nikē (victory) and laos (the people): hence, victory of the people. Cole, Ercole, Micole, Niocole, Nycole. Nicole is a feminine given name and a surname.The given name Nicole is of Greek origin and means victorious people. It's evolved into a French feminine derivative of the masculine...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+* Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
+  ```json
+  {
+      "pad_value": -1,
+      "activation_fct": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### 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: 10 characters</li><li>mean: 33.86 characters</li><li>max: 100 characters</li></ul> | <ul><li>size: 10 elements</li></ul> | <ul><li>size: 10 elements</li></ul> |
+* Samples:
+  | query                                                   | docs                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | labels                            |
+  |:--------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>how much does a adjunct professor get paid</code> | <code>['The average per-course pay reported for adjuncts at Ohio State University is $4,853, compared with an average of $6,500 reported at the University of Michigan at Ann Arbor. Harvard pays adjuncts $11,037, on average, according to the data that adjuncts have submitted so far. Many adjuncts have also indicated that they are essentially shut out of participating in most forms of governance. The overall average pay reported by adjuncts is $2,987 per three-credit course. Adjuncts at 16 colleges reported earning less than $1,000. The highest pay reported is $12,575, in the anthropology department at Harvard University', "Not surprisingly, at community colleges, adjuncts said they are paid much less. At Houston Community College, adjuncts reported earning between $1,200 and $2,200 for a three-credit English course. In some departments, adjuncts said anecdotally that pay depends on the degree held. One adjunct professor in history, for example, reported that where he or she works, instructors...</code> | <code>[1, 1, 0, 0, 0, ...]</code> |
+  | <code>what a normal heart beat per minute</code>        | <code>["1 A normal adult resting heart beat is between 60-100 heartbeats per minute. 2  Some experienced athletes may see their resting heartrate fall below 60 beats per minute. 3  Tachycardia refers to the heart beating too fast at rest-over 100 beats per minute. 1 Your heart rate is the number of times per minute that the heart beats. 2  Heart rate rises significantly in response to adrenaline if a person is frightened or surprised. 3  Taking a person's pulse is a direct measure of heart rate.", 'Most adults have a resting heart rate of 60-100 beats per minute (bpm). The fitter you are, the lower your resting heart rate is likely to be. For example, athletes may have a resting heart rate of 40-60 bpm or lower. ', 'Even if you’re not an athlete, knowledge about your heart rate can help you monitor your fitness level — and it might even help you spot developing health problems. Your heart rate, or pulse, is the number of times your heart beats per minute. Normal heart rate varies from person...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+  | <code>what is sauterne wine</code>                      | <code>['Sauternes is a French sweet wine from the Sauternais region of the Graves section in Bordeaux. Barsac lies within Sauternes, and is entitled to use either name. Somewhat similar but less expensive and typically less-distinguished wines are produced in the neighboring regions of Monbazillac, Cerons, Cérons loupiac And. cadillac', 'Sauternes is made from Semillon, Sémillon sauvignon, blanc And muscadelle grapes that have been affected By botrytis, cinerea also known as noble. rot Barsac lies within Sauternes, and is entitled to use either name. Somewhat similar but less expensive and typically less-distinguished wines are produced in the neighboring regions of Monbazillac, Cerons, Cérons loupiac And. cadillac', 'Sauternes, 40 miles (65km) south of Bordeaux city, is a village famous for its high-quality sweet wines. Although some wineries here produce dry wines, they sell them under appellations other than the sweet-specific Sauternes appellation. A half-bottle of top-quality, aged Saut...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
+* Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
+  ```json
+  {
+      "pad_value": -1,
+      "activation_fct": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 6
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `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`: 6
+- `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`: 3
+- `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_ndcg@10  | NanoNFCorpus_ndcg@10 | NanoNQ_ndcg@10       | NanoBEIR_R100_mean_ndcg@10 |
+|:----------:|:---------:|:-------------:|:---------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------------:|
+| -1         | -1        | -             | -               | 0.0644 (-0.4760)     | 0.2696 (-0.0554)     | 0.0762 (-0.4245)     | 0.1367 (-0.3186)           |
+| 0.0001     | 1         | 2.0486        | -               | -                    | -                    | -                    | -                          |
+| 0.0762     | 1000      | 2.0857        | -               | -                    | -                    | -                    | -                          |
+| 0.1525     | 2000      | 2.082         | -               | -                    | -                    | -                    | -                          |
+| 0.2287     | 3000      | 2.0758        | -               | -                    | -                    | -                    | -                          |
+| 0.3049     | 4000      | 2.0736        | 2.0711          | 0.5528 (+0.0124)     | 0.3638 (+0.0388)     | 0.6038 (+0.1032)     | 0.5068 (+0.0514)           |
+| 0.3812     | 5000      | 2.0735        | -               | -                    | -                    | -                    | -                          |
+| 0.4574     | 6000      | 2.0685        | -               | -                    | -                    | -                    | -                          |
+| 0.5336     | 7000      | 2.0725        | -               | -                    | -                    | -                    | -                          |
+| 0.6098     | 8000      | 2.0746        | 2.0698          | 0.5251 (-0.0153)     | 0.3446 (+0.0195)     | 0.5542 (+0.0535)     | 0.4746 (+0.0193)           |
+| 0.6861     | 9000      | 2.0759        | -               | -                    | -                    | -                    | -                          |
+| 0.7623     | 10000     | 2.0723        | -               | -                    | -                    | -                    | -                          |
+| 0.8385     | 11000     | 2.0787        | -               | -                    | -                    | -                    | -                          |
+| 0.9148     | 12000     | 2.0742        | 2.0697          | 0.5562 (+0.0157)     | 0.3700 (+0.0450)     | 0.6062 (+0.1056)     | 0.5108 (+0.0554)           |
+| 0.9910     | 13000     | 2.0736        | -               | -                    | -                    | -                    | -                          |
+| 1.0672     | 14000     | 2.0715        | -               | -                    | -                    | -                    | -                          |
+| 1.1435     | 15000     | 2.073         | -               | -                    | -                    | -                    | -                          |
+| 1.2197     | 16000     | 2.0699        | 2.0696          | 0.5532 (+0.0128)     | 0.3634 (+0.0384)     | 0.6355 (+0.1349)     | 0.5174 (+0.0620)           |
+| 1.2959     | 17000     | 2.0671        | -               | -                    | -                    | -                    | -                          |
+| 1.3722     | 18000     | 2.0682        | -               | -                    | -                    | -                    | -                          |
+| 1.4484     | 19000     | 2.0702        | -               | -                    | -                    | -                    | -                          |
+| **1.5246** | **20000** | **2.0699**    | **2.0695**      | **0.5787 (+0.0383)** | **0.3588 (+0.0337)** | **0.6221 (+0.1215)** | **0.5199 (+0.0645)**       |
+| 1.6009     | 21000     | 2.0689        | -               | -                    | -                    | -                    | -                          |
+| 1.6771     | 22000     | 2.0699        | -               | -                    | -                    | -                    | -                          |
+| 1.7533     | 23000     | 2.0667        | -               | -                    | -                    | -                    | -                          |
+| 1.8295     | 24000     | 2.0694        | 2.0694          | 0.5411 (+0.0006)     | 0.3817 (+0.0567)     | 0.6167 (+0.1161)     | 0.5132 (+0.0578)           |
+| 1.9058     | 25000     | 2.0632        | -               | -                    | -                    | -                    | -                          |
+| 1.9820     | 26000     | 2.0721        | -               | -                    | -                    | -                    | -                          |
+| 2.0582     | 27000     | 2.0647        | -               | -                    | -                    | -                    | -                          |
+| 2.1345     | 28000     | 2.0688        | 2.0702          | 0.5746 (+0.0342)     | 0.3845 (+0.0594)     | 0.5908 (+0.0901)     | 0.5166 (+0.0613)           |
+| 2.2107     | 29000     | 2.0635        | -               | -                    | -                    | -                    | -                          |
+| 2.2869     | 30000     | 2.0665        | -               | -                    | -                    | -                    | -                          |
+| 2.3632     | 31000     | 2.0643        | -               | -                    | -                    | -                    | -                          |
+| 2.4394     | 32000     | 2.0681        | 2.0699          | 0.5529 (+0.0125)     | 0.3690 (+0.0440)     | 0.5725 (+0.0718)     | 0.4981 (+0.0428)           |
+| 2.5156     | 33000     | 2.0642        | -               | -                    | -                    | -                    | -                          |
+| 2.5919     | 34000     | 2.0613        | -               | -                    | -                    | -                    | -                          |
+| 2.6681     | 35000     | 2.0673        | -               | -                    | -                    | -                    | -                          |
+| 2.7443     | 36000     | 2.0641        | 2.0696          | 0.5534 (+0.0130)     | 0.3701 (+0.0451)     | 0.5639 (+0.0632)     | 0.4958 (+0.0404)           |
+| 2.8206     | 37000     | 2.0645        | -               | -                    | -                    | -                    | -                          |
+| 2.8968     | 38000     | 2.0673        | -               | -                    | -                    | -                    | -                          |
+| 2.9730     | 39000     | 2.0656        | -               | -                    | -                    | -                    | -                          |
+| -1         | -1        | -             | -               | 0.5787 (+0.0383)     | 0.3588 (+0.0337)     | 0.6221 (+0.1215)     | 0.5199 (+0.0645)           |
+
+* The bold row denotes the saved checkpoint.
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.532 kWh
+- **Carbon Emitted**: 0.207 kg of CO2
+- **Hours Used**: 1.708 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 3.5.0.dev0
+- Transformers: 4.48.3
+- PyTorch: 2.5.0+cu121
+- Accelerate: 1.4.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### ListNetLoss
+```bibtex
+@inproceedings{cao2007learning,
+    title={Learning to rank: from pairwise approach to listwise approach},
+    author={Cao, Zhe and Qin, Tao and Liu, Tie-Yan and Tsai, Ming-Feng and Li, Hang},
+    booktitle={Proceedings of the 24th international conference on Machine learning},
+    pages={129--136},
+    year={2007}
+}
+```
+
+<!--
+## 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 +1,31 @@
+{
+  "_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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "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:eb79278d962aa7cca0c3c6c54103e31883db21d86a86421f7eeb958e2031e969
+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]"
+}