|
--- |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- loss:CosineSimilarityLoss |
|
base_model: google-bert/bert-base-uncased |
|
metrics: |
|
- pearson_cosine |
|
- spearman_cosine |
|
- pearson_manhattan |
|
- spearman_manhattan |
|
- pearson_euclidean |
|
- spearman_euclidean |
|
- pearson_dot |
|
- spearman_dot |
|
- pearson_max |
|
- spearman_max |
|
widget: |
|
- source_sentence: A man is speaking. |
|
sentences: |
|
- A man is talking on a phone. |
|
- The boy is jumping into a lake. |
|
- A cat is pouncing on a trampoline. |
|
- source_sentence: A woman is reading. |
|
sentences: |
|
- A woman is writing something. |
|
- A woman is applying eye shadow. |
|
- A tiger is walking around his cage. |
|
- source_sentence: A baby is laughing. |
|
sentences: |
|
- The baby laughed in his car seat. |
|
- A green bus drives down a road. |
|
- A woman is applying eye shadow. |
|
- source_sentence: A man jumping rope |
|
sentences: |
|
- A man is climbing a rope. |
|
- The boy is jumping into a lake. |
|
- Two women sitting in lawn chairs. |
|
- source_sentence: A person makes fire. |
|
sentences: |
|
- The person is starting a fire. |
|
- Blast on Indian train kills one |
|
- An animal is chewing on something. |
|
pipeline_tag: sentence-similarity |
|
co2_eq_emissions: |
|
emissions: 1.1600350080390396 |
|
energy_consumed: 0.002984381371948278 |
|
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: 0.014 |
|
hardware_used: 1 x NVIDIA GeForce RTX 3090 |
|
model-index: |
|
- name: SentenceTransformer based on google-bert/bert-base-uncased |
|
results: |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts dev |
|
type: sts-dev |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.8517529845876077 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8524623532914918 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.800899823827701 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8051568979113306 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8006826117948451 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8053116182840467 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7449289216960278 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7558824436512839 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8517529845876077 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8524623532914918 |
|
name: Spearman Max |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test |
|
type: sts-test |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.83020870287088 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.823188318981985 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8082481232573683 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8009567692854708 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8074730784388158 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8001358594920889 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7172194732542608 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7095712222240558 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.83020870287088 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.823188318981985 |
|
name: Spearman Max |
|
--- |
|
|
|
# SentenceTransformer based on google-bert/bert-base-uncased |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) |
|
- **Language:** en |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
|
(1): CNN( |
|
(convs): ModuleList( |
|
(0): Conv1d(768, 256, kernel_size=(1,), stride=(1,)) |
|
(1): Conv1d(768, 256, kernel_size=(3,), stride=(1,), padding=(1,)) |
|
(2): Conv1d(768, 256, kernel_size=(5,), stride=(1,), padding=(2,)) |
|
) |
|
) |
|
(2): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("tomaarsen/bert-base-uncased-cnn") |
|
# Run inference |
|
sentences = [ |
|
'A person makes fire.', |
|
'The person is starting a fire.', |
|
'Blast on Indian train kills one', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Semantic Similarity |
|
* Dataset: `sts-dev` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.8518 | |
|
| **spearman_cosine** | **0.8525** | |
|
| pearson_manhattan | 0.8009 | |
|
| spearman_manhattan | 0.8052 | |
|
| pearson_euclidean | 0.8007 | |
|
| spearman_euclidean | 0.8053 | |
|
| pearson_dot | 0.7449 | |
|
| spearman_dot | 0.7559 | |
|
| pearson_max | 0.8518 | |
|
| spearman_max | 0.8525 | |
|
|
|
#### Semantic Similarity |
|
* Dataset: `sts-test` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.8302 | |
|
| **spearman_cosine** | **0.8232** | |
|
| pearson_manhattan | 0.8082 | |
|
| spearman_manhattan | 0.801 | |
|
| pearson_euclidean | 0.8075 | |
|
| spearman_euclidean | 0.8001 | |
|
| pearson_dot | 0.7172 | |
|
| spearman_dot | 0.7096 | |
|
| pearson_max | 0.8302 | |
|
| spearman_max | 0.8232 | |
|
|
|
<!-- |
|
## 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 |
|
|
|
#### sentence-transformers/stsb |
|
|
|
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a) |
|
* Size: 5,749 training samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | score | |
|
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | score | |
|
|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| |
|
| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> | |
|
| <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> | |
|
| <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### sentence-transformers/stsb |
|
|
|
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a) |
|
* Size: 1,500 evaluation samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | score | |
|
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | score | |
|
|:--------------------------------------------------|:------------------------------------------------------|:------------------| |
|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | |
|
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | |
|
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: steps |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 32 |
|
- `num_train_epochs`: 1 |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: True |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: steps |
|
- `prediction_loss_only`: False |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 32 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 5e-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 |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: True |
|
- `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`: None |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
|
|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| |
|
| 0.5556 | 100 | 0.0417 | 0.0304 | 0.8525 | - | |
|
| 1.0 | 180 | - | - | - | 0.8232 | |
|
|
|
|
|
### Environmental Impact |
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
|
- **Energy Consumed**: 0.003 kWh |
|
- **Carbon Emitted**: 0.001 kg of CO2 |
|
- **Hours Used**: 0.014 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: 3.0.0.dev0 |
|
- Transformers: 4.41.0.dev0 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.26.1 |
|
- Datasets: 2.18.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
