|
--- |
|
base_model: indobenchmark/indobert-base-p2 |
|
datasets: |
|
- afaji/indonli |
|
language: |
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- id |
|
library_name: sentence-transformers |
|
metrics: |
|
- pearson_cosine |
|
- spearman_cosine |
|
- pearson_manhattan |
|
- spearman_manhattan |
|
- pearson_euclidean |
|
- spearman_euclidean |
|
- pearson_dot |
|
- spearman_dot |
|
- pearson_max |
|
- spearman_max |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:6915 |
|
- loss:SoftmaxLoss |
|
widget: |
|
- source_sentence: Pesta Olahraga Asia Tenggara atau Southeast Asian Games, biasa |
|
disingkat SEA Games, adalah ajang olahraga yang diadakan setiap dua tahun dan |
|
melibatkan 11 negara Asia Tenggara. |
|
sentences: |
|
- Sekarang tahun 2017. |
|
- Warna kulit tidak mempengaruhi waktu berjemur yang baik untuk mengatifkan pro-vitamin |
|
D3. |
|
- Pesta Olahraga Asia Tenggara diadakan setiap tahun. |
|
- source_sentence: Menjalani aktivitas Ramadhan di tengah wabah Corona tentunya tidak |
|
mudah. |
|
sentences: |
|
- Tidak ada observasi yang pernah dilansir oleh Business Insider. |
|
- Wabah Corona membuat aktivitas Ramadhan tidak mudah dijalani. |
|
- Piala Sudirman pertama digelar pada tahun 1989. |
|
- source_sentence: Dalam bidang politik, partai ini memperjuangkan agar kekuasaan |
|
sepenuhnya berada di tangan rakyat. |
|
sentences: |
|
- Galileo tidak berhasil mengetes hasil dari Hukum Inert. |
|
- Kudeta 14 Februari 1946 gagal merebut kekuasaan Belanda. |
|
- Partai ini berusaha agar kekuasaan sepenuhnya berada di tangan rakyat. |
|
- source_sentence: Keluarga mendiang Prince menuduh layanan musik streaming Tidal |
|
memasukkan karya milik sang penyanyi legendaris tanpa izin . |
|
sentences: |
|
- Rosier adalah pelayan setia Lord Voldemort. |
|
- Bangunan ini digunakan untuk penjualan. |
|
- Keluarga mendiang Prince sudah memberi izin kepada TImbal untuk menggunakan lagu |
|
milik Prince. |
|
- source_sentence: Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan |
|
respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum. |
|
sentences: |
|
- Pembuat Rooms hanya bisa membuat meeting yang terbuka. |
|
- Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat |
|
CRTC. |
|
- Eminem dirasa tidak akan memulai kembali kariernya tahun ini. |
|
model-index: |
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- name: SentenceTransformer based on indobenchmark/indobert-base-p2 |
|
results: |
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- task: |
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type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts dev |
|
type: sts-dev |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.6086483919467034 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.5957239631216208 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.5922712402608701 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.587803408019803 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.6025076942104072 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.5921960802996976 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.6142627736326208 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6070693135603054 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6142627736326208 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6070693135603054 |
|
name: Spearman Max |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test |
|
type: sts-test |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.3358355665097759 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.30366523911959453 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.2926304091437024 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.2892617235512195 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.307849173953621 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.29286510016277595 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.3501215321086179 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.33369282261837974 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.3501215321086179 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.33369282261837974 |
|
name: Spearman Max |
|
--- |
|
|
|
# SentenceTransformer based on indobenchmark/indobert-base-p2 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) 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:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) |
|
- **Language:** id |
|
<!-- - **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): 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("cassador/4bs4lr2") |
|
# Run inference |
|
sentences = [ |
|
'Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.', |
|
'Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC.', |
|
'Pembuat Rooms hanya bisa membuat meeting yang terbuka.', |
|
] |
|
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 |
|
* Dataset: `sts-dev` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.6086 | |
|
| **spearman_cosine** | **0.5957** | |
|
| pearson_manhattan | 0.5923 | |
|
| spearman_manhattan | 0.5878 | |
|
| pearson_euclidean | 0.6025 | |
|
| spearman_euclidean | 0.5922 | |
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| pearson_dot | 0.6143 | |
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| spearman_dot | 0.6071 | |
|
| pearson_max | 0.6143 | |
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| spearman_max | 0.6071 | |
|
|
|
#### Semantic Similarity |
|
* Dataset: `sts-test` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.3358 | |
|
| **spearman_cosine** | **0.3037** | |
|
| pearson_manhattan | 0.2926 | |
|
| spearman_manhattan | 0.2893 | |
|
| pearson_euclidean | 0.3078 | |
|
| spearman_euclidean | 0.2929 | |
|
| pearson_dot | 0.3501 | |
|
| spearman_dot | 0.3337 | |
|
| pearson_max | 0.3501 | |
|
| spearman_max | 0.3337 | |
|
|
|
<!-- |
|
## 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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|
|
<!-- |
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### Recommendations |
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|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### afaji/indonli |
|
|
|
* Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) |
|
* Size: 6,915 training samples |
|
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | premise | hypothesis | label | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
|
| type | string | string | int | |
|
| details | <ul><li>min: 12 tokens</li><li>mean: 29.26 tokens</li><li>max: 135 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.13 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~51.00%</li><li>1: ~49.00%</li></ul> | |
|
* Samples: |
|
| premise | hypothesis | label | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:---------------| |
|
| <code>Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.</code> | <code>Prediksi akhir wabah tidak disampaikan Jokowi.</code> | <code>0</code> | |
|
| <code>Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>Masker sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>1</code> | |
|
| <code>Seperti namanya, paket internet sahur Telkomsel ini ditujukan bagi pengguna yang menginginkan kuota ekstra, untuk menemani momen sahur sepanjang bulan puasa.</code> | <code>Paket internet sahur tidak ditujukan untuk saat sahur.</code> | <code>0</code> | |
|
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
|
|
|
### Evaluation Dataset |
|
|
|
#### afaji/indonli |
|
|
|
* Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) |
|
* Size: 1,556 evaluation samples |
|
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | premise | hypothesis | label | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
|
| type | string | string | int | |
|
| details | <ul><li>min: 9 tokens</li><li>mean: 28.07 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.15 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~47.90%</li><li>1: ~52.10%</li></ul> | |
|
* Samples: |
|
| premise | hypothesis | label | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------| |
|
| <code>Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020).</code> | <code>Manuskrip tersebut tidak mencatat laporan kematian.</code> | <code>0</code> | |
|
| <code>Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu.</code> | <code>Tidak ada observasi yang pernah dilansir oleh Business Insider.</code> | <code>0</code> | |
|
| <code>Seorang wanita asal New York mengaku sangat benci air putih.</code> | <code>Tidak ada orang dari New York yang membenci air putih.</code> | <code>0</code> | |
|
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 4 |
|
- `per_device_eval_batch_size`: 4 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: True |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 4 |
|
- `per_device_eval_batch_size`: 4 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_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`: 4 |
|
- `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 |
|
- `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 | 0 | - | - | 0.1277 | - | |
|
| 0.0578 | 100 | 0.706 | - | - | - | |
|
| 0.1157 | 200 | 0.6251 | - | - | - | |
|
| 0.1735 | 300 | 0.509 | - | - | - | |
|
| 0.2313 | 400 | 0.5822 | - | - | - | |
|
| 0.2892 | 500 | 0.6089 | - | - | - | |
|
| 0.3470 | 600 | 0.5497 | - | - | - | |
|
| 0.4049 | 700 | 0.6176 | - | - | - | |
|
| 0.4627 | 800 | 0.584 | - | - | - | |
|
| 0.5205 | 900 | 0.5317 | - | - | - | |
|
| 0.5784 | 1000 | 0.6706 | - | - | - | |
|
| 0.6362 | 1100 | 0.5508 | - | - | - | |
|
| 0.6940 | 1200 | 0.569 | - | - | - | |
|
| 0.7519 | 1300 | 0.6095 | - | - | - | |
|
| 0.8097 | 1400 | 0.5107 | - | - | - | |
|
| 0.8676 | 1500 | 0.5799 | - | - | - | |
|
| 0.9254 | 1600 | 0.5481 | - | - | - | |
|
| 0.9832 | 1700 | 0.4749 | - | - | - | |
|
| 1.0 | 1729 | - | 0.4679 | 0.5346 | - | |
|
| 1.0411 | 1800 | 0.4321 | - | - | - | |
|
| 1.0989 | 1900 | 0.4594 | - | - | - | |
|
| 1.1567 | 2000 | 0.4428 | - | - | - | |
|
| 1.2146 | 2100 | 0.479 | - | - | - | |
|
| 1.2724 | 2200 | 0.3944 | - | - | - | |
|
| 1.3302 | 2300 | 0.434 | - | - | - | |
|
| 1.3881 | 2400 | 0.3981 | - | - | - | |
|
| 1.4459 | 2500 | 0.5058 | - | - | - | |
|
| 1.5038 | 2600 | 0.4254 | - | - | - | |
|
| 1.5616 | 2700 | 0.5089 | - | - | - | |
|
| 1.6194 | 2800 | 0.4669 | - | - | - | |
|
| 1.6773 | 2900 | 0.5093 | - | - | - | |
|
| 1.7351 | 3000 | 0.4673 | - | - | - | |
|
| 1.7929 | 3100 | 0.4964 | - | - | - | |
|
| 1.8508 | 3200 | 0.366 | - | - | - | |
|
| 1.9086 | 3300 | 0.5168 | - | - | - | |
|
| 1.9665 | 3400 | 0.4976 | - | - | - | |
|
| 2.0 | 3458 | - | 0.4956 | 0.5756 | - | |
|
| 2.0243 | 3500 | 0.4112 | - | - | - | |
|
| 2.0821 | 3600 | 0.3139 | - | - | - | |
|
| 2.1400 | 3700 | 0.2579 | - | - | - | |
|
| 2.1978 | 3800 | 0.3207 | - | - | - | |
|
| 2.2556 | 3900 | 0.2962 | - | - | - | |
|
| 2.3135 | 4000 | 0.3924 | - | - | - | |
|
| 2.3713 | 4100 | 0.3059 | - | - | - | |
|
| 2.4291 | 4200 | 0.2762 | - | - | - | |
|
| 2.4870 | 4300 | 0.3425 | - | - | - | |
|
| 2.5448 | 4400 | 0.3165 | - | - | - | |
|
| 2.6027 | 4500 | 0.2786 | - | - | - | |
|
| 2.6605 | 4600 | 0.3183 | - | - | - | |
|
| 2.7183 | 4700 | 0.4492 | - | - | - | |
|
| 2.7762 | 4800 | 0.2414 | - | - | - | |
|
| 2.8340 | 4900 | 0.3064 | - | - | - | |
|
| 2.8918 | 5000 | 0.3164 | - | - | - | |
|
| 2.9497 | 5100 | 0.2612 | - | - | - | |
|
| 3.0 | 5187 | - | 0.8414 | 0.6116 | - | |
|
| 3.0075 | 5200 | 0.318 | - | - | - | |
|
| 3.0654 | 5300 | 0.201 | - | - | - | |
|
| 3.1232 | 5400 | 0.1045 | - | - | - | |
|
| 3.1810 | 5500 | 0.1038 | - | - | - | |
|
| 3.2389 | 5600 | 0.1365 | - | - | - | |
|
| 3.2967 | 5700 | 0.1279 | - | - | - | |
|
| 3.3545 | 5800 | 0.2304 | - | - | - | |
|
| 3.4124 | 5900 | 0.1515 | - | - | - | |
|
| 3.4702 | 6000 | 0.1682 | - | - | - | |
|
| 3.5281 | 6100 | 0.2008 | - | - | - | |
|
| 3.5859 | 6200 | 0.1955 | - | - | - | |
|
| 3.6437 | 6300 | 0.103 | - | - | - | |
|
| 3.7016 | 6400 | 0.1482 | - | - | - | |
|
| 3.7594 | 6500 | 0.1093 | - | - | - | |
|
| 3.8172 | 6600 | 0.1478 | - | - | - | |
|
| 3.8751 | 6700 | 0.1708 | - | - | - | |
|
| 3.9329 | 6800 | 0.2399 | - | - | - | |
|
| 3.9907 | 6900 | 0.1805 | - | - | - | |
|
| 4.0 | 6916 | - | 1.0672 | 0.5957 | 0.3037 | |
|
|
|
|
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### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
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### BibTeX |
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#### Sentence Transformers and SoftmaxLoss |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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