sentence-transformers safetensors bert cross-encoder text-classification generated_from_trainer dataset_size:578402 loss:BinaryCrossEntropyLoss en arxiv:1908.10084 base_model:prajjwal1/bert-tiny base_model:finetune:prajjwal1/bert-tiny license:apache-2.0 co2_eq_emissions region:us
---
base_model: prajjwal1/bert-tiny
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- map
- mrr@10
- ndcg@10
pipeline_tag: text-classification
tags:
- sentence-transformers
- cross-encoder
- text-classification
- generated_from_trainer
- dataset_size:578402
- loss:BinaryCrossEntropyLoss
co2_eq_emissions:
  emissions: 7.3866990525881215
  energy_consumed: 0.019003501532248668
  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: 0.099
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: BERT-tiny trained on GooAQ
  results: []
---

# BERT-tiny trained on GooAQ

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) 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.

This model was trained using [train_script.py](train_script.py).

## Model Details

### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) <!-- at revision 6f75de8b60a9f8a2fdf7b69cbd86d9e64bcb3837 -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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("cross-encoder-testing/reranker-bert-tiny-gooaq-bce")
# Get scores for pairs of texts
pairs = [
    ['are javascript developers in demand?', "JavaScript is the skill that is most in-demand for IT in 2020, according to a report from developer skills tester DevSkiller. The report, “Top IT Skills report 2020: Demand and Hiring Trends,” has JavaScript switching places with Java when compared to last year's report, with Java in third place this year, behind SQL."],
    ['are javascript developers in demand?', 'In one line difference between the two is: JavaScript is the programming language where as AngularJS is a framework based on JavaScript. ... It is also the basic for all java script based technologies like jquery, angular JS, bootstrap JS and so on. Angular JS is a framework written in javascript and uses MVC architecture.'],
    ['are javascript developers in demand?', 'Java applications are run in a virtual machine or web browser while JavaScript is run on a web browser. Java code is compiled whereas while JavaScript code is in text and in a web page. JavaScript is an OOP scripting language, whereas Java is an OOP programming language.'],
    ['are javascript developers in demand?', 'Things in the body tag are the things that should be displayed: the actual content. Javascript in the body is executed as it is read and as the page is rendered. Javascript in the head is interpreted before anything is rendered.'],
    ['are javascript developers in demand?', 'Web apps tend to be built using JavaScript, CSS and HTML5. Unlike mobile apps, there is no standard software development kit for building web apps. However, developers do have access to templates. Compared to mobile apps, web apps are usually quicker and easier to build — but they are much simpler in terms of features.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'are javascript developers in demand?',
    [
        "JavaScript is the skill that is most in-demand for IT in 2020, according to a report from developer skills tester DevSkiller. The report, “Top IT Skills report 2020: Demand and Hiring Trends,” has JavaScript switching places with Java when compared to last year's report, with Java in third place this year, behind SQL.",
        'In one line difference between the two is: JavaScript is the programming language where as AngularJS is a framework based on JavaScript. ... It is also the basic for all java script based technologies like jquery, angular JS, bootstrap JS and so on. Angular JS is a framework written in javascript and uses MVC architecture.',
        'Java applications are run in a virtual machine or web browser while JavaScript is run on a web browser. Java code is compiled whereas while JavaScript code is in text and in a web page. JavaScript is an OOP scripting language, whereas Java is an OOP programming language.',
        'Things in the body tag are the things that should be displayed: the actual content. Javascript in the body is executed as it is read and as the page is rendered. Javascript in the head is interpreted before anything is rendered.',
        'Web apps tend to be built using JavaScript, CSS and HTML5. Unlike mobile apps, there is no standard software development kit for building web apps. However, developers do have access to templates. Compared to mobile apps, web apps are usually quicker and easier to build — but they are much simpler in terms of features.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Cross Encoder Reranking

* Datasets: `gooaq-dev`, `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      | gooaq-dev            | NanoMSMARCO          | NanoNFCorpus         | NanoNQ               |
|:------------|:---------------------|:---------------------|:---------------------|:---------------------|
| map         | 0.5677 (+0.0366)     | 0.4280 (-0.0616)     | 0.3397 (+0.0787)     | 0.4149 (-0.0047)     |
| mrr@10      | 0.5558 (+0.0318)     | 0.4129 (-0.0646)     | 0.5196 (+0.0198)     | 0.4132 (-0.0135)     |
| **ndcg@10** | **0.6157 (+0.0245)** | **0.4772 (-0.0632)** | **0.3308 (+0.0058)** | **0.4859 (-0.0147)** |

#### 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         | 0.3942 (+0.0041)     |
| mrr@10      | 0.4486 (-0.0194)     |
| **ndcg@10** | **0.4313 (-0.0241)** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 578,402 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                                       | answer                                                                                           | label                                           |
  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                                         | string                                                                                           | int                                             |
  | details | <ul><li>min: 21 characters</li><li>mean: 43.81 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 252.46 characters</li><li>max: 405 characters</li></ul> | <ul><li>0: ~82.90%</li><li>1: ~17.10%</li></ul> |
* Samples:
  | question                                          | answer                                                                                                                                                                                                                                                                                                                                            | label          |
  |:--------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>are javascript developers in demand?</code> | <code>JavaScript is the skill that is most in-demand for IT in 2020, according to a report from developer skills tester DevSkiller. The report, “Top IT Skills report 2020: Demand and Hiring Trends,” has JavaScript switching places with Java when compared to last year's report, with Java in third place this year, behind SQL.</code>      | <code>1</code> |
  | <code>are javascript developers in demand?</code> | <code>In one line difference between the two is: JavaScript is the programming language where as AngularJS is a framework based on JavaScript. ... It is also the basic for all java script based technologies like jquery, angular JS, bootstrap JS and so on. Angular JS is a framework written in javascript and uses MVC architecture.</code> | <code>0</code> |
  | <code>are javascript developers in demand?</code> | <code>Java applications are run in a virtual machine or web browser while JavaScript is run on a web browser. Java code is compiled whereas while JavaScript code is in text and in a web page. JavaScript is an OOP scripting language, whereas Java is an OOP programming language.</code>                                                      | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
  ```json
  {
      "activation_fct": "torch.nn.modules.linear.Identity",
      "pos_weight": 5
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `learning_rate`: 0.0005
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: 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`: 2048
- `per_device_eval_batch_size`: 2048
- `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`: 0.0005
- `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`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `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 | gooaq-dev_ndcg@10 | NanoMSMARCO_ndcg@10 | NanoNFCorpus_ndcg@10 | NanoNQ_ndcg@10   | NanoBEIR_R100_mean_ndcg@10 |
|:------:|:----:|:-------------:|:-----------------:|:-------------------:|:--------------------:|:----------------:|:--------------------------:|
| -1     | -1   | -             | 0.0887 (-0.5025)  | 0.0063 (-0.5341)    | 0.3262 (+0.0012)     | 0.0000 (-0.5006) | 0.1108 (-0.3445)           |
| 0.0035 | 1    | 1.1945        | -                 | -                   | -                    | -                | -                          |
| 0.0707 | 20   | 1.1664        | 0.4082 (-0.1830)  | 0.1805 (-0.3600)    | 0.3168 (-0.0083)     | 0.2243 (-0.2763) | 0.2405 (-0.2149)           |
| 0.1413 | 40   | 1.1107        | 0.5260 (-0.0652)  | 0.3453 (-0.1951)    | 0.3335 (+0.0085)     | 0.3430 (-0.1576) | 0.3406 (-0.1147)           |
| 0.2120 | 60   | 1.022         | 0.5623 (-0.0289)  | 0.3929 (-0.1475)    | 0.3512 (+0.0262)     | 0.3472 (-0.1535) | 0.3638 (-0.0916)           |
| 0.2827 | 80   | 0.973         | 0.5691 (-0.0221)  | 0.4048 (-0.1356)    | 0.3530 (+0.0280)     | 0.3833 (-0.1174) | 0.3804 (-0.0750)           |
| 0.3534 | 100  | 0.963         | 0.5814 (-0.0098)  | 0.4385 (-0.1019)    | 0.3471 (+0.0221)     | 0.4227 (-0.0779) | 0.4028 (-0.0526)           |
| 0.4240 | 120  | 0.9419        | 0.5963 (+0.0050)  | 0.4106 (-0.1298)    | 0.3540 (+0.0289)     | 0.4843 (-0.0163) | 0.4163 (-0.0391)           |
| 0.4947 | 140  | 0.9331        | 0.5953 (+0.0041)  | 0.4310 (-0.1094)    | 0.3367 (+0.0117)     | 0.4163 (-0.0843) | 0.3947 (-0.0607)           |
| 0.5654 | 160  | 0.9263        | 0.6070 (+0.0158)  | 0.4626 (-0.0778)    | 0.3443 (+0.0193)     | 0.4823 (-0.0184) | 0.4297 (-0.0256)           |
| 0.6360 | 180  | 0.9212        | 0.6069 (+0.0156)  | 0.4602 (-0.0802)    | 0.3391 (+0.0141)     | 0.4782 (-0.0224) | 0.4258 (-0.0295)           |
| 0.7067 | 200  | 0.901         | 0.6126 (+0.0214)  | 0.4602 (-0.0803)    | 0.3413 (+0.0162)     | 0.4780 (-0.0227) | 0.4265 (-0.0289)           |
| 0.7774 | 220  | 0.8997        | 0.6136 (+0.0224)  | 0.4801 (-0.0604)    | 0.3349 (+0.0098)     | 0.4903 (-0.0103) | 0.4351 (-0.0203)           |
| 0.8481 | 240  | 0.9021        | 0.6132 (+0.0220)  | 0.4850 (-0.0554)    | 0.3438 (+0.0188)     | 0.4855 (-0.0151) | 0.4381 (-0.0173)           |
| 0.9187 | 260  | 0.9013        | 0.6188 (+0.0276)  | 0.4820 (-0.0584)    | 0.3387 (+0.0137)     | 0.4851 (-0.0156) | 0.4353 (-0.0201)           |
| 0.9894 | 280  | 0.8996        | 0.6157 (+0.0245)  | 0.4772 (-0.0632)    | 0.3305 (+0.0054)     | 0.4859 (-0.0147) | 0.4312 (-0.0242)           |
| -1     | -1   | -             | 0.6157 (+0.0245)  | 0.4772 (-0.0632)    | 0.3308 (+0.0058)     | 0.4859 (-0.0147) | 0.4313 (-0.0241)           |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.019 kWh
- **Carbon Emitted**: 0.007 kg of CO2
- **Hours Used**: 0.099 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: 1.3.0
- Datasets: 2.20.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

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