diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1738 @@ +--- +language: +- hu +license: apache-2.0 +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:1044013 +- loss:MultipleNegativesRankingLoss +base_model: tasksource/ModernBERT-base-nli +widget: +- source_sentence: Emberek várnak a lámpánál kerékpárral. + sentences: + - Az emberek piros lámpánál haladnak. + - Az emberek a kerékpárjukon vannak. + - Egy fekete kutya úszik a vízben egy teniszlabdával a szájában +- source_sentence: A kutya a vízben van. + sentences: + - Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik pedig + a tetőn. + - A macska a vízben van, és dühös. + - Egy kutya van a vízben, a szájában egy faág. +- source_sentence: A nő feketét visel. + sentences: + - Egy barna kutya fröcsköl, ahogy úszik a vízben. + - Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre. + - 'Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A képen:' +- source_sentence: Az emberek alszanak. + sentences: + - Három ember beszélget egy városi utcán. + - A nő fehéret visel. + - Egy apa és a fia ölelgeti alvás közben. +- source_sentence: Az emberek alszanak. + sentences: + - Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében, miközben + egy idősebb nő átmegy az utcán. + - Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy + sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős + elmosódás tesz kivehetetlenné. + - Egy apa és a fia ölelgeti alvás közben. +pipeline_tag: sentence-similarity +library_name: sentence-transformers +metrics: +- cosine_accuracy +model-index: +- name: ModernBERT-base + results: + - task: + type: triplet + name: Triplet + dataset: + name: all nli dev + type: all-nli-dev + metrics: + - type: cosine_accuracy + value: 0.7102 + name: Cosine Accuracy + - task: + type: triplet + name: Triplet + dataset: + name: all nli test + type: all-nli-test + metrics: + - type: cosine_accuracy + value: 0.67 + name: Cosine Accuracy +--- + +# ModernBERT-base + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tasksource/ModernBERT-base-nli](https://huggingface.co/tasksource/ModernBERT-base-nli) on the train 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:** [tasksource/ModernBERT-base-nli](https://huggingface.co/tasksource/ModernBERT-base-nli) +- **Maximum Sequence Length:** 2048 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity +- **Training Dataset:** + - train +- **Language:** hu +- **License:** apache-2.0 + +### 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': 2048, 'do_lower_case': False}) with Transformer model: ModernBertModel + (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("karsar/ModernBERT-base-hu") +# Run inference +sentences = [ + 'Az emberek alszanak.', + 'Egy apa és a fia ölelgeti alvás közben.', + 'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.', +] +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] +``` + + + + + + + +## Evaluation + +### Metrics + +#### Triplet + +* Datasets: `all-nli-dev` and `all-nli-test` +* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) + +| Metric | all-nli-dev | all-nli-test | +|:--------------------|:------------|:-------------| +| **cosine_accuracy** | **0.7102** | **0.67** | + + + + + +## Training Details + +### Training Dataset + +#### train + +* Dataset: train +* Size: 1,044,013 training samples +* Columns: anchor, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | anchor | positive | negative | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | anchor | positive | negative | + |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------| + | Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett. | Egy ember a szabadban, lóháton. | Egy ember egy étteremben van, és omlettet rendel. | + | Gyerekek mosolyogva és integetett a kamera | Gyermekek vannak jelen | A gyerekek homlokot rántanak | + | Egy fiú ugrál a gördeszkát a közepén egy piros híd. | A fiú gördeszkás trükköt csinál. | A fiú korcsolyázik a járdán. | +* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "cos_sim" + } + ``` + +### Evaluation Dataset + +#### train + +* Dataset: train +* Size: 5,000 evaluation samples +* Columns: anchor, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | anchor | positive | negative | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | anchor | positive | negative | + |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------| + | Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett. | Egy ember a szabadban, lóháton. | Egy ember egy étteremben van, és omlettet rendel. | + | Gyerekek mosolyogva és integetett a kamera | Gyermekek vannak jelen | A gyerekek homlokot rántanak | + | Egy fiú ugrál a gördeszkát a közepén egy piros híd. | A fiú gördeszkás trükköt csinál. | A fiú korcsolyázik a járdán. | +* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "cos_sim" + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `num_train_epochs`: 1 +- `warmup_ratio`: 0.1 +- `bf16`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 8 +- `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`: 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 +- `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`: 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`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | train loss | all-nli-dev_cosine_accuracy | all-nli-test_cosine_accuracy | +|:------:|:------:|:-------------:|:----------:|:---------------------------:|:----------------------------:| +| 0 | 0 | - | - | 0.5306 | - | +| 0.0008 | 100 | 2.818 | - | - | - | +| 0.0015 | 200 | 2.5967 | - | - | - | +| 0.0023 | 300 | 2.5905 | - | - | - | +| 0.0031 | 400 | 2.4971 | - | - | - | +| 0.0038 | 500 | 2.5099 | - | - | - | +| 0.0046 | 600 | 2.3072 | - | - | - | +| 0.0054 | 700 | 1.883 | - | - | - | +| 0.0061 | 800 | 1.3657 | - | - | - | +| 0.0069 | 900 | 1.5772 | - | - | - | +| 0.0077 | 1000 | 1.2804 | - | - | - | +| 0.0084 | 1100 | 1.3332 | - | - | - | +| 0.0092 | 1200 | 1.0596 | - | - | - | +| 0.0100 | 1300 | 0.7385 | - | - | - | +| 0.0107 | 1400 | 0.8946 | - | - | - | +| 0.0115 | 1500 | 0.7507 | - | - | - | +| 0.0123 | 1600 | 0.7161 | - | - | - | +| 0.0130 | 1700 | 0.7176 | - | - | - | +| 0.0138 | 1800 | 0.8173 | - | - | - | +| 0.0146 | 1900 | 0.7464 | - | - | - | +| 0.0153 | 2000 | 0.7542 | 0.9124 | 0.7244 | - | +| 0.0161 | 2100 | 0.6147 | - | - | - | +| 0.0169 | 2200 | 0.5755 | - | - | - | +| 0.0176 | 2300 | 1.0103 | - | - | - | +| 0.0184 | 2400 | 0.6152 | - | - | - | +| 0.0192 | 2500 | 0.4283 | - | - | - | +| 0.0199 | 2600 | 0.5124 | - | - | - | +| 0.0207 | 2700 | 0.7056 | - | - | - | +| 0.0215 | 2800 | 0.4688 | - | - | - | +| 0.0222 | 2900 | 0.5029 | - | - | - | +| 0.0230 | 3000 | 0.5196 | - | - | - | +| 0.0238 | 3100 | 0.488 | - | - | - | +| 0.0245 | 3200 | 0.3601 | - | - | - | +| 0.0253 | 3300 | 0.6175 | - | - | - | +| 0.0261 | 3400 | 0.7494 | - | - | - | +| 0.0268 | 3500 | 0.5092 | - | - | - | +| 0.0276 | 3600 | 0.557 | - | - | - | +| 0.0284 | 3700 | 0.9289 | - | - | - | +| 0.0291 | 3800 | 0.6672 | - | - | - | +| 0.0299 | 3900 | 0.6498 | - | - | - | +| 0.0307 | 4000 | 0.3891 | 1.6045 | 0.6146 | - | +| 0.0314 | 4100 | 0.3863 | - | - | - | +| 0.0322 | 4200 | 0.6232 | - | - | - | +| 0.0329 | 4300 | 0.7755 | - | - | - | +| 0.0337 | 4400 | 0.4809 | - | - | - | +| 0.0345 | 4500 | 0.2968 | - | - | - | +| 0.0352 | 4600 | 0.3402 | - | - | - | +| 0.0360 | 4700 | 0.4622 | - | - | - | +| 0.0368 | 4800 | 0.7333 | - | - | - | +| 0.0375 | 4900 | 0.5718 | - | - | - | +| 0.0383 | 5000 | 0.7179 | - | - | - | +| 0.0391 | 5100 | 0.5597 | - | - | - | +| 0.0398 | 5200 | 0.4884 | - | - | - | +| 0.0406 | 5300 | 0.3467 | - | - | - | +| 0.0414 | 5400 | 0.4234 | - | - | - | +| 0.0421 | 5500 | 0.4149 | - | - | - | +| 0.0429 | 5600 | 0.4568 | - | - | - | +| 0.0437 | 5700 | 0.5416 | - | - | - | +| 0.0444 | 5800 | 0.4317 | - | - | - | +| 0.0452 | 5900 | 0.5537 | - | - | - | +| 0.0460 | 6000 | 0.5437 | 1.2609 | 0.6716 | - | +| 0.0467 | 6100 | 0.526 | - | - | - | +| 0.0475 | 6200 | 0.5891 | - | - | - | +| 0.0483 | 6300 | 0.5615 | - | - | - | +| 0.0490 | 6400 | 0.6012 | - | - | - | +| 0.0498 | 6500 | 0.6295 | - | - | - | +| 0.0506 | 6600 | 0.7951 | - | - | - | +| 0.0513 | 6700 | 0.7898 | - | - | - | +| 0.0521 | 6800 | 0.523 | - | - | - | +| 0.0529 | 6900 | 0.5871 | - | - | - | +| 0.0536 | 7000 | 0.6337 | - | - | - | +| 0.0544 | 7100 | 0.7428 | - | - | - | +| 0.0552 | 7200 | 0.6299 | - | - | - | +| 0.0559 | 7300 | 0.389 | - | - | - | +| 0.0567 | 7400 | 0.384 | - | - | - | +| 0.0575 | 7500 | 0.7069 | - | - | - | +| 0.0582 | 7600 | 0.5387 | - | - | - | +| 0.0590 | 7700 | 0.4936 | - | - | - | +| 0.0598 | 7800 | 0.579 | - | - | - | +| 0.0605 | 7900 | 0.8806 | - | - | - | +| 0.0613 | 8000 | 0.7165 | 1.3063 | 0.6654 | - | +| 0.0621 | 8100 | 0.5884 | - | - | - | +| 0.0628 | 8200 | 0.7272 | - | - | - | +| 0.0636 | 8300 | 0.8061 | - | - | - | +| 0.0644 | 8400 | 0.6705 | - | - | - | +| 0.0651 | 8500 | 0.79 | - | - | - | +| 0.0659 | 8600 | 1.0937 | - | - | - | +| 0.0667 | 8700 | 0.7903 | - | - | - | +| 0.0674 | 8800 | 0.423 | - | - | - | +| 0.0682 | 8900 | 0.8756 | - | - | - | +| 0.0690 | 9000 | 0.7461 | - | - | - | +| 0.0697 | 9100 | 0.7282 | - | - | - | +| 0.0705 | 9200 | 0.7343 | - | - | - | +| 0.0713 | 9300 | 0.5109 | - | - | - | +| 0.0720 | 9400 | 0.5142 | - | - | - | +| 0.0728 | 9500 | 0.6688 | - | - | - | +| 0.0736 | 9600 | 0.8799 | - | - | - | +| 0.0743 | 9700 | 0.6665 | - | - | - | +| 0.0751 | 9800 | 0.6979 | - | - | - | +| 0.0759 | 9900 | 0.6971 | - | - | - | +| 0.0766 | 10000 | 0.8392 | 1.5133 | 0.703 | - | +| 0.0774 | 10100 | 0.6283 | - | - | - | +| 0.0782 | 10200 | 0.6315 | - | - | - | +| 0.0789 | 10300 | 0.4937 | - | - | - | +| 0.0797 | 10400 | 0.4819 | - | - | - | +| 0.0805 | 10500 | 0.5177 | - | - | - | +| 0.0812 | 10600 | 0.637 | - | - | - | +| 0.0820 | 10700 | 0.584 | - | - | - | +| 0.0828 | 10800 | 0.9142 | - | - | - | +| 0.0835 | 10900 | 0.6953 | - | - | - | +| 0.0843 | 11000 | 0.7623 | - | - | - | +| 0.0851 | 11100 | 0.6357 | - | - | - | +| 0.0858 | 11200 | 0.7508 | - | - | - | +| 0.0866 | 11300 | 0.5425 | - | - | - | +| 0.0874 | 11400 | 0.596 | - | - | - | +| 0.0881 | 11500 | 0.8407 | - | - | - | +| 0.0889 | 11600 | 0.7463 | - | - | - | +| 0.0897 | 11700 | 0.9188 | - | - | - | +| 0.0904 | 11800 | 0.6921 | - | - | - | +| 0.0912 | 11900 | 0.7707 | - | - | - | +| 0.0920 | 12000 | 0.7206 | 1.6151 | 0.6568 | - | +| 0.0927 | 12100 | 0.7925 | - | - | - | +| 0.0935 | 12200 | 0.8842 | - | - | - | +| 0.0943 | 12300 | 0.8328 | - | - | - | +| 0.0950 | 12400 | 0.5571 | - | - | - | +| 0.0958 | 12500 | 0.9304 | - | - | - | +| 0.0966 | 12600 | 0.4566 | - | - | - | +| 0.0973 | 12700 | 0.5217 | - | - | - | +| 0.0981 | 12800 | 0.4589 | - | - | - | +| 0.0988 | 12900 | 0.4216 | - | - | - | +| 0.0996 | 13000 | 0.7141 | - | - | - | +| 0.1004 | 13100 | 0.6205 | - | - | - | +| 0.1011 | 13200 | 0.4045 | - | - | - | +| 0.1019 | 13300 | 0.3494 | - | - | - | +| 0.1027 | 13400 | 0.4802 | - | - | - | +| 0.1034 | 13500 | 0.4482 | - | - | - | +| 0.1042 | 13600 | 0.5367 | - | - | - | +| 0.1050 | 13700 | 0.3565 | - | - | - | +| 0.1057 | 13800 | 0.3069 | - | - | - | +| 0.1065 | 13900 | 0.3576 | - | - | - | +| 0.1073 | 14000 | 0.4572 | 0.6264 | 0.8504 | - | +| 0.1080 | 14100 | 0.3922 | - | - | - | +| 0.1088 | 14200 | 0.289 | - | - | - | +| 0.1096 | 14300 | 0.5305 | - | - | - | +| 0.1103 | 14400 | 0.5243 | - | - | - | +| 0.1111 | 14500 | 0.5738 | - | - | - | +| 0.1119 | 14600 | 0.3457 | - | - | - | +| 0.1126 | 14700 | 0.3254 | - | - | - | +| 0.1134 | 14800 | 0.6328 | - | - | - | +| 0.1142 | 14900 | 0.4711 | - | - | - | +| 0.1149 | 15000 | 0.2532 | - | - | - | +| 0.1157 | 15100 | 0.4379 | - | - | - | +| 0.1165 | 15200 | 0.4992 | - | - | - | +| 0.1172 | 15300 | 0.3239 | - | - | - | +| 0.1180 | 15400 | 0.3294 | - | - | - | +| 0.1188 | 15500 | 0.332 | - | - | - | +| 0.1195 | 15600 | 0.3025 | - | - | - | +| 0.1203 | 15700 | 0.2406 | - | - | - | +| 0.1211 | 15800 | 0.4625 | - | - | - | +| 0.1218 | 15900 | 0.5237 | - | - | - | +| 0.1226 | 16000 | 0.3451 | 1.2647 | 0.8454 | - | +| 0.1234 | 16100 | 0.5763 | - | - | - | +| 0.1241 | 16200 | 0.8095 | - | - | - | +| 0.1249 | 16300 | 0.5725 | - | - | - | +| 0.1257 | 16400 | 0.5191 | - | - | - | +| 0.1264 | 16500 | 0.3933 | - | - | - | +| 0.1272 | 16600 | 0.3892 | - | - | - | +| 0.1280 | 16700 | 0.5239 | - | - | - | +| 0.1287 | 16800 | 0.6505 | - | - | - | +| 0.1295 | 16900 | 0.3977 | - | - | - | +| 0.1303 | 17000 | 0.2333 | - | - | - | +| 0.1310 | 17100 | 0.3542 | - | - | - | +| 0.1318 | 17200 | 0.3516 | - | - | - | +| 0.1326 | 17300 | 0.5825 | - | - | - | +| 0.1333 | 17400 | 0.4237 | - | - | - | +| 0.1341 | 17500 | 0.5338 | - | - | - | +| 0.1349 | 17600 | 0.3754 | - | - | - | +| 0.1356 | 17700 | 0.4027 | - | - | - | +| 0.1364 | 17800 | 0.3067 | - | - | - | +| 0.1372 | 17900 | 0.4103 | - | - | - | +| 0.1379 | 18000 | 0.3567 | 1.5228 | 0.7474 | - | +| 0.1387 | 18100 | 0.356 | - | - | - | +| 0.1395 | 18200 | 0.6599 | - | - | - | +| 0.1402 | 18300 | 0.4607 | - | - | - | +| 0.1410 | 18400 | 0.5707 | - | - | - | +| 0.1418 | 18500 | 0.506 | - | - | - | +| 0.1425 | 18600 | 0.553 | - | - | - | +| 0.1433 | 18700 | 0.4427 | - | - | - | +| 0.1441 | 18800 | 0.4758 | - | - | - | +| 0.1448 | 18900 | 0.4573 | - | - | - | +| 0.1456 | 19000 | 0.5183 | - | - | - | +| 0.1464 | 19100 | 0.7152 | - | - | - | +| 0.1471 | 19200 | 0.6519 | - 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| +| 0.1663 | 21700 | 0.6138 | - | - | - | +| 0.1670 | 21800 | 0.5235 | - | - | - | +| 0.1678 | 21900 | 0.5078 | - | - | - | +| 0.1686 | 22000 | 0.7041 | 1.3245 | 0.7018 | - | +| 0.1693 | 22100 | 0.8529 | - | - | - | +| 0.1701 | 22200 | 0.5939 | - | - | - | +| 0.1709 | 22300 | 0.642 | - | - | - | +| 0.1716 | 22400 | 0.5834 | - | - | - | +| 0.1724 | 22500 | 0.7157 | - | - | - | +| 0.1732 | 22600 | 0.5561 | - | - | - | +| 0.1739 | 22700 | 0.5861 | - | - | - | +| 0.1747 | 22800 | 0.4327 | - | - | - | +| 0.1755 | 22900 | 0.409 | - | - | - | +| 0.1762 | 23000 | 0.462 | - | - | - | +| 0.1770 | 23100 | 0.581 | - | - | - | +| 0.1778 | 23200 | 0.5704 | - | - | - | +| 0.1785 | 23300 | 0.8111 | - | - | - | +| 0.1793 | 23400 | 0.6905 | - | - | - | +| 0.1801 | 23500 | 0.6811 | - | - | - | +| 0.1808 | 23600 | 0.6078 | - | - | - | +| 0.1816 | 23700 | 0.6502 | - | - | - | +| 0.1824 | 23800 | 0.5575 | - | - | - | +| 0.1831 | 23900 | 0.5162 | - | - | - | +| 0.1839 | 24000 | 0.7487 | 1.5347 | 0.7036 | - | +| 0.1847 | 24100 | 0.7011 | - | - | - | +| 0.1854 | 24200 | 0.7417 | - | - | - | +| 0.1862 | 24300 | 0.6514 | - | - | - | +| 0.1870 | 24400 | 0.738 | - | - | - | +| 0.1877 | 24500 | 0.7296 | - | - | - | +| 0.1885 | 24600 | 0.6939 | - | - | - | +| 0.1893 | 24700 | 0.8072 | - | - | - | +| 0.1900 | 24800 | 0.7847 | - | - | - | +| 0.1908 | 24900 | 0.5243 | - | - | - | +| 0.1916 | 25000 | 0.8317 | - | - | - | +| 0.1923 | 25100 | 0.3981 | - | - | - | +| 0.1931 | 25200 | 0.4715 | - | - | - | +| 0.1939 | 25300 | 0.3734 | - | - | - | +| 0.1946 | 25400 | 0.43 | - | - | - | +| 0.1954 | 25500 | 0.6921 | - | - | - | +| 0.1962 | 25600 | 0.724 | - | - | - | +| 0.1969 | 25700 | 0.4203 | - | - | - | +| 0.1977 | 25800 | 0.3013 | - | - | - | +| 0.1985 | 25900 | 0.5666 | - | - | - | +| 0.1992 | 26000 | 0.454 | 0.5471 | 0.9028 | - | +| 0.2000 | 26100 | 0.4989 | - | - | - | +| 0.2008 | 26200 | 0.4614 | - | - | - | +| 0.2015 | 26300 | 0.2856 | - | - | - | +| 0.2023 | 26400 | 0.458 | - | - | - | +| 0.2031 | 26500 | 0.5247 | - 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| +| 0.2222 | 29000 | 0.2632 | - | - | - | +| 0.2230 | 29100 | 0.2601 | - | - | - | +| 0.2238 | 29200 | 0.4946 | - | - | - | +| 0.2245 | 29300 | 0.5398 | - | - | - | +| 0.2253 | 29400 | 0.3971 | - | - | - | +| 0.2261 | 29500 | 0.2474 | - | - | - | +| 0.2268 | 29600 | 0.2803 | - | - | - | +| 0.2276 | 29700 | 0.3461 | - | - | - | +| 0.2283 | 29800 | 0.569 | - | - | - | +| 0.2291 | 29900 | 0.3645 | - | - | - | +| 0.2299 | 30000 | 0.4675 | 0.7793 | 0.82 | - | +| 0.2306 | 30100 | 0.3114 | - | - | - | +| 0.2314 | 30200 | 0.2872 | - | - | - | +| 0.2322 | 30300 | 0.2524 | - | - | - | +| 0.2329 | 30400 | 0.3419 | - | - | - | +| 0.2337 | 30500 | 0.3456 | - | - | - | +| 0.2345 | 30600 | 0.2813 | - | - | - | +| 0.2352 | 30700 | 0.6423 | - | - | - | +| 0.2360 | 30800 | 0.5166 | - | - | - | +| 0.2368 | 30900 | 0.6356 | - | - | - | +| 0.2375 | 31000 | 0.6066 | - | - | - | +| 0.2383 | 31100 | 0.3955 | - | - | - | +| 0.2391 | 31200 | 0.4736 | - | - | - | +| 0.2398 | 31300 | 0.468 | - | - | - | +| 0.2406 | 31400 | 0.4473 | - 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| - | - | +| 0.8421 | 109900 | 0.9045 | - | - | - | +| 0.8429 | 110000 | 0.926 | 1.2050 | 0.7128 | - | +| 0.8437 | 110100 | 0.9332 | - | - | - | +| 0.8444 | 110200 | 0.9144 | - | - | - | +| 0.8452 | 110300 | 0.8979 | - | - | - | +| 0.8460 | 110400 | 0.9142 | - | - | - | +| 0.8467 | 110500 | 0.8362 | - | - | - | +| 0.8475 | 110600 | 0.9579 | - | - | - | +| 0.8483 | 110700 | 0.8762 | - | - | - | +| 0.8490 | 110800 | 0.8719 | - | - | - | +| 0.8498 | 110900 | 0.9494 | - | - | - | +| 0.8506 | 111000 | 0.8743 | - | - | - | +| 0.8513 | 111100 | 0.9266 | - | - | - | +| 0.8521 | 111200 | 0.8943 | - | - | - | +| 0.8529 | 111300 | 0.908 | - | - | - | +| 0.8536 | 111400 | 0.8316 | - | - | - | +| 0.8544 | 111500 | 0.8574 | - | - | - | +| 0.8552 | 111600 | 0.8587 | - | - | - | +| 0.8559 | 111700 | 0.8862 | - | - | - | +| 0.8567 | 111800 | 0.8303 | - | - | - | +| 0.8575 | 111900 | 0.8252 | - | - | - | +| 0.8582 | 112000 | 0.8726 | 1.2884 | 0.704 | - | +| 0.8590 | 112100 | 0.8788 | - | - | - | +| 0.8598 | 112200 | 0.8197 | - | - | - | +| 0.8605 | 112300 | 0.8406 | - | - | - | +| 0.8613 | 112400 | 0.8634 | - | - | - | +| 0.8621 | 112500 | 0.8347 | - | - | - | +| 0.8628 | 112600 | 0.8558 | - | - | - | +| 0.8636 | 112700 | 0.8313 | - | - | - | +| 0.8644 | 112800 | 0.8221 | - | - | - | +| 0.8651 | 112900 | 0.8508 | - | - | - | +| 0.8659 | 113000 | 0.8214 | - | - | - | +| 0.8667 | 113100 | 0.8185 | - | - | - | +| 0.8674 | 113200 | 0.8198 | - | - | - | +| 0.8682 | 113300 | 0.8681 | - | - | - | +| 0.8690 | 113400 | 0.8676 | - | - | - | +| 0.8697 | 113500 | 0.838 | - | - | - | +| 0.8705 | 113600 | 0.8215 | - | - | - | +| 0.8713 | 113700 | 0.8468 | - | - | - | +| 0.8720 | 113800 | 0.807 | - | - | - | +| 0.8728 | 113900 | 0.8392 | - | - | - | +| 0.8735 | 114000 | 0.8318 | 1.2201 | 0.7272 | - | +| 0.8743 | 114100 | 0.824 | - | - | - | +| 0.8751 | 114200 | 0.7816 | - | - | - | +| 0.8758 | 114300 | 0.8095 | - | - | - | +| 0.8766 | 114400 | 0.8412 | - | - | - | +| 0.8774 | 114500 | 0.8514 | - | - | - | +| 0.8781 | 114600 | 0.836 | - | - | - | +| 0.8789 | 114700 | 0.8375 | - | - | - | +| 0.8797 | 114800 | 0.8267 | - | - | - | +| 0.8804 | 114900 | 0.7925 | - | - | - | +| 0.8812 | 115000 | 0.7948 | - | - | - | +| 0.8820 | 115100 | 0.7908 | - | - | - | +| 0.8827 | 115200 | 0.8092 | - | - | - | +| 0.8835 | 115300 | 0.7942 | - | - | - | +| 0.8843 | 115400 | 0.7551 | - | - | - | +| 0.8850 | 115500 | 0.7717 | - | - | - | +| 0.8858 | 115600 | 0.764 | - | - | - | +| 0.8866 | 115700 | 0.8025 | - | - | - | +| 0.8873 | 115800 | 0.8182 | - | - | - | +| 0.8881 | 115900 | 0.7158 | - | - | - | +| 0.8889 | 116000 | 0.7632 | 1.2520 | 0.7258 | - | +| 0.8896 | 116100 | 0.7593 | - | - | - | +| 0.8904 | 116200 | 0.8032 | - | - | - | +| 0.8912 | 116300 | 0.7809 | - | - | - | +| 0.8919 | 116400 | 0.7251 | - | - | - | +| 0.8927 | 116500 | 0.802 | - | - | - | +| 0.8935 | 116600 | 0.7949 | - | - | - | +| 0.8942 | 116700 | 0.7827 | - | - | - | +| 0.8950 | 116800 | 0.79 | - | - | - | +| 0.8958 | 116900 | 0.7668 | - | - | - | +| 0.8965 | 117000 | 0.8076 | - | - | - | +| 0.8973 | 117100 | 0.7533 | - | - | - | +| 0.8981 | 117200 | 0.752 | - | - | - | +| 0.8988 | 117300 | 0.7225 | - | - | - | +| 0.8996 | 117400 | 0.7694 | - | - | - | +| 0.9004 | 117500 | 0.7941 | - | - | - | +| 0.9011 | 117600 | 0.7548 | - | - | - | +| 0.9019 | 117700 | 0.7617 | - | - | - | +| 0.9027 | 117800 | 0.7693 | - | - | - | +| 0.9034 | 117900 | 0.7304 | - | - | - | +| 0.9042 | 118000 | 0.7955 | 1.2687 | 0.7324 | - | +| 0.9050 | 118100 | 0.7567 | - | - | - | +| 0.9057 | 118200 | 0.7253 | - | - | - | +| 0.9065 | 118300 | 0.756 | - | - | - | +| 0.9073 | 118400 | 0.7583 | - | - | - | +| 0.9080 | 118500 | 0.7559 | - | - | - | +| 0.9088 | 118600 | 0.7462 | - | - | - | +| 0.9096 | 118700 | 0.7433 | - | - | - | +| 0.9103 | 118800 | 0.7701 | - | - | - | +| 0.9111 | 118900 | 0.7541 | - | - | - | +| 0.9119 | 119000 | 0.7084 | - | - | - | +| 0.9126 | 119100 | 0.7207 | - | - | - | +| 0.9134 | 119200 | 0.7435 | - | - | - | +| 0.9142 | 119300 | 0.7528 | - | - | - | +| 0.9149 | 119400 | 0.7179 | - | - | - | +| 0.9157 | 119500 | 0.7276 | - | - | - | +| 0.9165 | 119600 | 0.7307 | - | - | - | +| 0.9172 | 119700 | 0.7484 | - | - | - | +| 0.9180 | 119800 | 1.1472 | - | - | - | +| 0.9188 | 119900 | 1.318 | - | - | - | +| 0.9195 | 120000 | 1.4042 | 1.2499 | 0.7238 | - | +| 0.9203 | 120100 | 1.2455 | - | - | - | +| 0.9211 | 120200 | 1.3026 | - | - | - | +| 0.9218 | 120300 | 1.0223 | - | - | - | +| 0.9226 | 120400 | 1.0531 | - | - | - | +| 0.9234 | 120500 | 1.1072 | - | - | - | +| 0.9241 | 120600 | 1.0879 | - | - | - | +| 0.9249 | 120700 | 1.0663 | - | - | - | +| 0.9257 | 120800 | 0.9457 | - | - | - | +| 0.9264 | 120900 | 1.1087 | - | - | - | +| 0.9272 | 121000 | 1.0896 | - | - | - | +| 0.9280 | 121100 | 1.0722 | - | - | - | +| 0.9287 | 121200 | 1.0552 | - | - | - | +| 0.9295 | 121300 | 1.1524 | - | - | - | +| 0.9303 | 121400 | 1.0925 | - | - | - | +| 0.9310 | 121500 | 1.0797 | - | - | - | +| 0.9318 | 121600 | 1.0849 | - | - | - | +| 0.9326 | 121700 | 1.0821 | - | - | - | +| 0.9333 | 121800 | 1.0373 | - | - | - | +| 0.9341 | 121900 | 1.1126 | - | - | - | +| 0.9349 | 122000 | 1.1339 | 1.2329 | 0.7186 | - | +| 0.9356 | 122100 | 1.1165 | - | - | - | +| 0.9364 | 122200 | 1.0295 | - | - | - | +| 0.9372 | 122300 | 0.9693 | - | - | - | +| 0.9379 | 122400 | 0.9914 | - | - | - | +| 0.9387 | 122500 | 0.9932 | - | - | - | +| 0.9394 | 122600 | 0.9796 | - | - | - | +| 0.9402 | 122700 | 0.9148 | - | - | - | +| 0.9410 | 122800 | 1.0079 | - | - | - | +| 0.9417 | 122900 | 0.9644 | - | - | - | +| 0.9425 | 123000 | 0.975 | - | - | - | +| 0.9433 | 123100 | 0.9657 | - | - | - | +| 0.9440 | 123200 | 0.944 | - | - | - | +| 0.9448 | 123300 | 1.0591 | - | - | - | +| 0.9456 | 123400 | 1.0175 | - | - | - | +| 0.9463 | 123500 | 0.9659 | - | - | - | +| 0.9471 | 123600 | 1.0315 | - | - | - | +| 0.9479 | 123700 | 0.9716 | - | - | - | +| 0.9486 | 123800 | 0.9887 | - | - | - | +| 0.9494 | 123900 | 0.9873 | - | - | - | +| 0.9502 | 124000 | 1.006 | 1.2707 | 0.7128 | - | +| 0.9509 | 124100 | 0.978 | - | - | - | +| 0.9517 | 124200 | 0.9194 | - | - | - | +| 0.9525 | 124300 | 0.8852 | - | - | - | +| 0.9532 | 124400 | 0.9265 | - | - | - | +| 0.9540 | 124500 | 0.956 | - | - | - | +| 0.9548 | 124600 | 0.87 | - | - | - | +| 0.9555 | 124700 | 0.8636 | - | - | - | +| 0.9563 | 124800 | 0.8749 | - | - | - | +| 0.9571 | 124900 | 0.8762 | - | - | - | +| 0.9578 | 125000 | 0.8432 | - | - | - | +| 0.9586 | 125100 | 0.8887 | - | - | - | +| 0.9594 | 125200 | 0.9097 | - | - | - | +| 0.9601 | 125300 | 0.823 | - | - | - | +| 0.9609 | 125400 | 0.8976 | - | - | - | +| 0.9617 | 125500 | 1.1025 | - | - | - | +| 0.9624 | 125600 | 0.9468 | - | - | - | +| 0.9632 | 125700 | 0.9733 | - | - | - | +| 0.9640 | 125800 | 0.9353 | - | - | - | +| 0.9647 | 125900 | 0.8633 | - | - | - | +| 0.9655 | 126000 | 0.9196 | 1.2893 | 0.7106 | - | +| 0.9663 | 126100 | 0.8514 | - | - | - | +| 0.9670 | 126200 | 0.8513 | - | - | - | +| 0.9678 | 126300 | 0.8597 | - | - | - | +| 0.9686 | 126400 | 0.929 | - | - | - | +| 0.9693 | 126500 | 0.9397 | - | - | - | +| 0.9701 | 126600 | 1.1423 | - | - | - | +| 0.9709 | 126700 | 1.0804 | - | - | - | +| 0.9716 | 126800 | 1.035 | - | - | - | +| 0.9724 | 126900 | 1.046 | - | - | - | +| 0.9732 | 127000 | 1.0308 | - | - | - | +| 0.9739 | 127100 | 0.985 | - | - | - | +| 0.9747 | 127200 | 1.0493 | - | - | - | +| 0.9755 | 127300 | 1.6257 | - | - | - | +| 0.9762 | 127400 | 0.9725 | - | - | - | +| 0.9770 | 127500 | 0.907 | - | - | - | +| 0.9778 | 127600 | 0.7219 | - | - | - | +| 0.9785 | 127700 | 0.7454 | - | - | - | +| 0.9793 | 127800 | 0.8149 | - | - | - | +| 0.9801 | 127900 | 0.6161 | - | - | - | +| 0.9808 | 128000 | 0.6559 | 1.3419 | 0.7106 | - | +| 0.9816 | 128100 | 0.6442 | - | - | - | +| 0.9824 | 128200 | 0.6461 | - | - | - | +| 0.9831 | 128300 | 0.7225 | - | - | - | +| 0.9839 | 128400 | 0.7092 | - | - | - | +| 0.9847 | 128500 | 0.7962 | - | - | - | +| 0.9854 | 128600 | 0.6362 | - | - | - | +| 0.9862 | 128700 | 0.815 | - | - | - | +| 0.9870 | 128800 | 0.7114 | - | - | - | +| 0.9877 | 128900 | 0.699 | - | - | - | +| 0.9885 | 129000 | 0.6741 | - | - | - | +| 0.9893 | 129100 | 0.5924 | - | - | - | +| 0.9900 | 129200 | 0.6305 | - | - | - | +| 0.9908 | 129300 | 0.9403 | - | - | - | +| 0.9916 | 129400 | 0.6837 | - | - | - | +| 0.9923 | 129500 | 0.8546 | - | - | - | +| 0.9931 | 129600 | 0.9752 | - | - | - | +| 0.9939 | 129700 | 0.9003 | - | - | - | +| 0.9946 | 129800 | 0.9449 | - | - | - | +| 0.9954 | 129900 | 0.871 | - | - | - | +| 0.9962 | 130000 | 0.7516 | 1.3455 | 0.7102 | - | +| 0.9969 | 130100 | 0.9024 | - | - | - | +| 0.9977 | 130200 | 0.8981 | - | - | - | +| 0.9985 | 130300 | 1.247 | - | - | - | +| 0.9992 | 130400 | 0.9558 | - | - | - | +| 1.0000 | 130500 | 0.9919 | - | - | - | +| 1.0 | 130502 | - | - | - | 0.67 | + +
+ +### Framework Versions +- Python: 3.10.12 +- Sentence Transformers: 3.3.1 +- Transformers: 4.48.0.dev0 +- PyTorch: 2.5.1+cu124 +- Accelerate: 1.2.1 +- Datasets: 3.2.0 +- 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", +} +``` + +#### MultipleNegativesRankingLoss +```bibtex +@misc{henderson2017efficient, + title={Efficient Natural Language Response Suggestion for Smart Reply}, + author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, + year={2017}, + eprint={1705.00652}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + + + + + + \ No newline at end of file