|
--- |
|
base_model: sentence-transformers/all-mpnet-base-v2 |
|
library_name: sentence-transformers |
|
metrics: |
|
- pearson_cosine |
|
- spearman_cosine |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:2353 |
|
- loss:CosineSimilarityLoss |
|
widget: |
|
- source_sentence: A year has passed since "The Black Rebellion" and the remaining |
|
Black Knights have vanished into the shadows, their leader and figurehead, Zero, |
|
executed by the Britannian Empire. Area 11 is once more squirming under the Emperors |
|
oppressive heel as the Britannian armies concentrate their attacks on the European |
|
front. But for the Britannians living in Area 11, life is back to normal. On one |
|
such normal day, a Britannian student, skipping his classes in the Ashford Academy, |
|
sneaks out to gamble on his chess play. But unknown to this young man, several |
|
forces are eying him from the shadows, for soon, he will experience a shocking |
|
encounter with his own obscured past, and the masked rebel mastermind Zero will |
|
return. |
|
sentences: |
|
- Politics |
|
- Mythology |
|
- Disability |
|
- source_sentence: 'In a land where corruption rules and a ruthless Prime Minister |
|
has turned the puppet Emperors armies of soldiers, assassins and secret police |
|
against the people, only one force dares to stand against them: Night Raid, an |
|
elite team of relentless killers, each equipped with an Imperial Arm - legendary |
|
weapons with unique and incredible powers created in the distant past.' |
|
sentences: |
|
- Kuudere |
|
- Tragedy |
|
- Seinen |
|
- source_sentence: Theres a rumor about a mysterious phenomenon called "puberty syndrome." |
|
For example, Sakuta Azusagawa is a high school student who suddenly sees a bunny |
|
girl appear in front of him. The girl is actually a girl named Mai Sakurajima, |
|
who is Sakutas upperclassman who is also a famous actress who has gone on hiatus |
|
from the entertainment industry. For some reason, the people around Mai cannot |
|
see her bunny-girl figure. Sakuta sets out to solve this mystery, and as he spends |
|
time with Mai, he learns her secret feelings. Other heroines who have "puberty |
|
syndrome" start to appear in front of Sakuta. |
|
sentences: |
|
- Heterosexual |
|
- Drama |
|
- Episodic |
|
- source_sentence: Dororo, a young orphan thief, meets Hyakkimaru, a powerful ronin. |
|
Hyakkimarus father, a greedy feudal lord, had made a pact with 12 demons, offering |
|
his yet-unborn sons body parts in exchange for great power. Thus, Hyakkimaru - |
|
who was born without arms, legs, eyes, ears, a nose or a mouth - was abandoned |
|
in a river as a baby. Rescued and raised by Dr. Honma, who equips him with artificial |
|
limbs and teaches him sword-fighting techniques, Hyakkimaru discovers that each |
|
time he slays a demon, a piece of his body is restored. Now, he roams the war-torn |
|
countryside in search of demons. |
|
sentences: |
|
- Urban |
|
- Heterosexual |
|
- Demons |
|
- source_sentence: Everyone has a part of themselves they cannot show to anyone else. |
|
sentences: |
|
- Transgender |
|
- Crime |
|
- Comedy |
|
model-index: |
|
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
|
results: |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: anime recommendation dev |
|
type: anime-recommendation-dev |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.6144532877889222 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6215240802205049 |
|
name: Spearman Cosine |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: anime recommendation test |
|
type: anime-recommendation-test |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.6535704432727567 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6393952594394526 |
|
name: Spearman Cosine |
|
--- |
|
|
|
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 --> |
|
- **Maximum Sequence Length:** 384 tokens |
|
- **Output Dimensionality:** 768 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel |
|
(1): 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}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## 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("Prashasst/anime-recommendation-model") |
|
# Run inference |
|
sentences = [ |
|
'I want anime like onepiece.', |
|
'Pirates', |
|
'Action', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, 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 |
|
|
|
* Datasets: `anime-recommendation-dev` and `anime-recommendation-test` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | anime-recommendation-dev | anime-recommendation-test | |
|
|:--------------------|:-------------------------|:--------------------------| |
|
| pearson_cosine | 0.6145 | 0.6536 | |
|
| **spearman_cosine** | **0.6215** | **0.6394** | |
|
|
|
<!-- |
|
## 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 |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 2,353 training samples |
|
* Columns: <code>description</code>, <code>genre</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | description | genre | label | |
|
|:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 15 tokens</li><li>mean: 97.39 tokens</li><li>max: 193 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.82 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.71</li><li>max: 1.0</li></ul> | |
|
* Samples: |
|
| description | genre | label | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------|:------------------| |
|
| <code>Mitsuha Miyamizu, a high school girl, yearns to live the life of a boy in the bustling city of Tokyo—a dream that stands in stark contrast to her present life in the countryside. Meanwhile in the city, Taki Tachibana lives a busy life as a high school student while juggling his part-time job and hopes for a future in architecture.</code> | <code>Environmental</code> | <code>0.6</code> | |
|
| <code>Jinta Yadomi and his group of childhood friends have become estranged after a tragic accident split them apart. Now in their high school years, a sudden surprise forces each of them to confront their guilt over what happened that day and come to terms with the ghosts of their past.</code> | <code>Hikikomori</code> | <code>0.79</code> | |
|
| <code>The second season of <i>Ansatsu Kyoushitsu</i>.</code> | <code>Episodic</code> | <code>0.44</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 294 evaluation samples |
|
* Columns: <code>description</code>, <code>genre</code>, and <code>label</code> |
|
* Approximate statistics based on the first 294 samples: |
|
| | description | genre | label | |
|
|:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 15 tokens</li><li>mean: 92.48 tokens</li><li>max: 193 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.73 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.69</li><li>max: 1.0</li></ul> | |
|
* Samples: |
|
| description | genre | label | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------|:------------------| |
|
| <code>Summer is here, and the heroes of Class 1-A and 1-B are in for the toughest training camp of their lives A group of seasoned pros pushes everyones Quirks to new heights as the students face one overwhelming challenge after another. Braving the elements in this secret location becomes the least of their worries when routine training turns into a critical struggle for survival.</code> | <code>Transgender</code> | <code>0.2</code> | |
|
| <code>"In order for something to be obtained, something of equal value must be lost."</code> | <code>Cyborg</code> | <code>0.72</code> | |
|
| <code>In the story, Subaru Natsuki is an ordinary high school student who is lost in an alternate world, where he is rescued by a beautiful, silver-haired girl. He stays near her to return the favor, but the destiny she is burdened with is more than Subaru can imagine. Enemies attack one by one, and both of them are killed. He then finds out he has the power to rewind death, back to the time he first came to this world. But only he remembers what has happened since.</code> | <code>Primarily Female Cast</code> | <code>0.61</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/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`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `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`: True |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 8 |
|
- `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`: 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`: 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`: 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_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `eval_use_gather_object`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | Validation Loss | anime-recommendation-dev_spearman_cosine | anime-recommendation-test_spearman_cosine | |
|
|:------:|:----:|:-------------:|:---------------:|:----------------------------------------:|:-----------------------------------------:| |
|
| 0.0068 | 1 | 0.3882 | - | - | - | |
|
| 0.0135 | 2 | 0.2697 | - | - | - | |
|
| 0.0203 | 3 | 0.2648 | - | - | - | |
|
| 0.0270 | 4 | 0.3022 | - | - | - | |
|
| 0.0338 | 5 | 0.2665 | - | - | - | |
|
| 0.0405 | 6 | 0.2923 | - | - | - | |
|
| 0.0473 | 7 | 0.3165 | - | - | - | |
|
| 0.0541 | 8 | 0.2069 | - | - | - | |
|
| 0.0608 | 9 | 0.271 | - | - | - | |
|
| 0.0676 | 10 | 0.1974 | - | - | - | |
|
| 0.0743 | 11 | 0.156 | - | - | - | |
|
| 0.0811 | 12 | 0.1035 | - | - | - | |
|
| 0.0878 | 13 | 0.1046 | - | - | - | |
|
| 0.0946 | 14 | 0.0579 | - | - | - | |
|
| 0.1014 | 15 | 0.0904 | - | - | - | |
|
| 0.1081 | 16 | 0.0734 | - | - | - | |
|
| 0.1149 | 17 | 0.0396 | - | - | - | |
|
| 0.1216 | 18 | 0.0219 | - | - | - | |
|
| 0.1284 | 19 | 0.0672 | - | - | - | |
|
| 0.1351 | 20 | 0.0567 | - | - | - | |
|
| 0.1419 | 21 | 0.0969 | - | - | - | |
|
| 0.1486 | 22 | 0.0258 | - | - | - | |
|
| 0.1554 | 23 | 0.1174 | - | - | - | |
|
| 0.1622 | 24 | 0.0334 | - | - | - | |
|
| 0.1689 | 25 | 0.0661 | - | - | - | |
|
| 0.1757 | 26 | 0.0365 | - | - | - | |
|
| 0.1824 | 27 | 0.049 | - | - | - | |
|
| 0.1892 | 28 | 0.0889 | - | - | - | |
|
| 0.1959 | 29 | 0.0179 | - | - | - | |
|
| 0.2027 | 30 | 0.0255 | - | - | - | |
|
| 0.2095 | 31 | 0.0312 | - | - | - | |
|
| 0.2162 | 32 | 0.0312 | - | - | - | |
|
| 0.2230 | 33 | 0.0619 | - | - | - | |
|
| 0.2297 | 34 | 0.0358 | - | - | - | |
|
| 0.2365 | 35 | 0.0468 | - | - | - | |
|
| 0.2432 | 36 | 0.0601 | - | - | - | |
|
| 0.25 | 37 | 0.0546 | - | - | - | |
|
| 0.2568 | 38 | 0.0411 | - | - | - | |
|
| 0.2635 | 39 | 0.0332 | - | - | - | |
|
| 0.2703 | 40 | 0.0479 | - | - | - | |
|
| 0.2770 | 41 | 0.0657 | - | - | - | |
|
| 0.2838 | 42 | 0.0161 | - | - | - | |
|
| 0.2905 | 43 | 0.0323 | - | - | - | |
|
| 0.2973 | 44 | 0.0794 | - | - | - | |
|
| 0.3041 | 45 | 0.0264 | - | - | - | |
|
| 0.3108 | 46 | 0.0391 | - | - | - | |
|
| 0.3176 | 47 | 0.0514 | - | - | - | |
|
| 0.3243 | 48 | 0.0276 | - | - | - | |
|
| 0.3311 | 49 | 0.0653 | - | - | - | |
|
| 0.3378 | 50 | 0.0343 | - | - | - | |
|
| 0.3446 | 51 | 0.0369 | - | - | - | |
|
| 0.3514 | 52 | 0.0336 | - | - | - | |
|
| 0.3581 | 53 | 0.0368 | - | - | - | |
|
| 0.3649 | 54 | 0.0477 | - | - | - | |
|
| 0.3716 | 55 | 0.0358 | - | - | - | |
|
| 0.3784 | 56 | 0.0312 | - | - | - | |
|
| 0.3851 | 57 | 0.0388 | - | - | - | |
|
| 0.3919 | 58 | 0.0415 | - | - | - | |
|
| 0.3986 | 59 | 0.02 | - | - | - | |
|
| 0.4054 | 60 | 0.0459 | - | - | - | |
|
| 0.4122 | 61 | 0.0302 | - | - | - | |
|
| 0.4189 | 62 | 0.0519 | - | - | - | |
|
| 0.4257 | 63 | 0.0283 | - | - | - | |
|
| 0.4324 | 64 | 0.04 | - | - | - | |
|
| 0.4392 | 65 | 0.0146 | - | - | - | |
|
| 0.4459 | 66 | 0.033 | - | - | - | |
|
| 0.4527 | 67 | 0.0365 | - | - | - | |
|
| 0.4595 | 68 | 0.0579 | - | - | - | |
|
| 0.4662 | 69 | 0.0253 | - | - | - | |
|
| 0.4730 | 70 | 0.033 | - | - | - | |
|
| 0.4797 | 71 | 0.0258 | - | - | - | |
|
| 0.4865 | 72 | 0.0181 | - | - | - | |
|
| 0.4932 | 73 | 0.0334 | - | - | - | |
|
| 0.5 | 74 | 0.0415 | - | - | - | |
|
| 0.5068 | 75 | 0.0258 | - | - | - | |
|
| 0.5135 | 76 | 0.0304 | - | - | - | |
|
| 0.5203 | 77 | 0.0211 | - | - | - | |
|
| 0.5270 | 78 | 0.0334 | - | - | - | |
|
| 0.5338 | 79 | 0.0278 | - | - | - | |
|
| 0.5405 | 80 | 0.0209 | - | - | - | |
|
| 0.5473 | 81 | 0.0391 | - | - | - | |
|
| 0.5541 | 82 | 0.0274 | - | - | - | |
|
| 0.5608 | 83 | 0.0213 | - | - | - | |
|
| 0.5676 | 84 | 0.0293 | - | - | - | |
|
| 0.5743 | 85 | 0.0205 | - | - | - | |
|
| 0.5811 | 86 | 0.0258 | - | - | - | |
|
| 0.5878 | 87 | 0.0262 | - | - | - | |
|
| 0.5946 | 88 | 0.0109 | - | - | - | |
|
| 0.6014 | 89 | 0.0268 | - | - | - | |
|
| 0.6081 | 90 | 0.0304 | - | - | - | |
|
| 0.6149 | 91 | 0.0328 | - | - | - | |
|
| 0.6216 | 92 | 0.0173 | - | - | - | |
|
| 0.6284 | 93 | 0.0253 | - | - | - | |
|
| 0.6351 | 94 | 0.0245 | - | - | - | |
|
| 0.6419 | 95 | 0.0232 | - | - | - | |
|
| 0.6486 | 96 | 0.0309 | - | - | - | |
|
| 0.6554 | 97 | 0.0209 | - | - | - | |
|
| 0.6622 | 98 | 0.0169 | - | - | - | |
|
| 0.6689 | 99 | 0.024 | - | - | - | |
|
| 0.6757 | 100 | 0.0166 | 0.0284 | 0.6215 | - | |
|
| 0.6824 | 101 | 0.0202 | - | - | - | |
|
| 0.6892 | 102 | 0.0181 | - | - | - | |
|
| 0.6959 | 103 | 0.0413 | - | - | - | |
|
| 0.7027 | 104 | 0.0537 | - | - | - | |
|
| 0.7095 | 105 | 0.0241 | - | - | - | |
|
| 0.7162 | 106 | 0.0199 | - | - | - | |
|
| 0.7230 | 107 | 0.0227 | - | - | - | |
|
| 0.7297 | 108 | 0.0283 | - | - | - | |
|
| 0.7365 | 109 | 0.0372 | - | - | - | |
|
| 0.7432 | 110 | 0.0193 | - | - | - | |
|
| 0.75 | 111 | 0.0147 | - | - | - | |
|
| 0.7568 | 112 | 0.0594 | - | - | - | |
|
| 0.7635 | 113 | 0.0185 | - | - | - | |
|
| 0.7703 | 114 | 0.0674 | - | - | - | |
|
| 0.7770 | 115 | 0.0212 | - | - | - | |
|
| 0.7838 | 116 | 0.0268 | - | - | - | |
|
| 0.7905 | 117 | 0.0233 | - | - | - | |
|
| 0.7973 | 118 | 0.0276 | - | - | - | |
|
| 0.8041 | 119 | 0.0242 | - | - | - | |
|
| 0.8108 | 120 | 0.034 | - | - | - | |
|
| 0.8176 | 121 | 0.0231 | - | - | - | |
|
| 0.8243 | 122 | 0.0252 | - | - | - | |
|
| 0.8311 | 123 | 0.0294 | - | - | - | |
|
| 0.8378 | 124 | 0.0205 | - | - | - | |
|
| 0.8446 | 125 | 0.0302 | - | - | - | |
|
| 0.8514 | 126 | 0.0468 | - | - | - | |
|
| 0.8581 | 127 | 0.0311 | - | - | - | |
|
| 0.8649 | 128 | 0.0365 | - | - | - | |
|
| 0.8716 | 129 | 0.0257 | - | - | - | |
|
| 0.8784 | 130 | 0.0339 | - | - | - | |
|
| 0.8851 | 131 | 0.0359 | - | - | - | |
|
| 0.8919 | 132 | 0.0404 | - | - | - | |
|
| 0.8986 | 133 | 0.0223 | - | - | - | |
|
| 0.9054 | 134 | 0.0232 | - | - | - | |
|
| 0.9122 | 135 | 0.0295 | - | - | - | |
|
| 0.9189 | 136 | 0.0244 | - | - | - | |
|
| 0.9257 | 137 | 0.0168 | - | - | - | |
|
| 0.9324 | 138 | 0.0319 | - | - | - | |
|
| 0.9392 | 139 | 0.0328 | - | - | - | |
|
| 0.9459 | 140 | 0.0295 | - | - | - | |
|
| 0.9527 | 141 | 0.0262 | - | - | - | |
|
| 0.9595 | 142 | 0.0238 | - | - | - | |
|
| 0.9662 | 143 | 0.0181 | - | - | - | |
|
| 0.9730 | 144 | 0.017 | - | - | - | |
|
| 0.9797 | 145 | 0.0244 | - | - | - | |
|
| 0.9865 | 146 | 0.0264 | - | - | - | |
|
| 0.9932 | 147 | 0.0194 | - | - | - | |
|
| 1.0 | 148 | 0.0028 | - | - | 0.6394 | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 0.34.2 |
|
- Datasets: 3.2.0 |
|
- 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.* |
|
--> |
