Update README.md

README.md

@@ -1,100 +1,6 @@
 ---
 base_model: intfloat/multilingual-e5-large
 library_name: sentence-transformers
-metrics:
-- negative_mse
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:22076
-- loss:MSELoss
-widget:
-- source_sentence: 'passage: Nagpadala ang Navy ng 16 Warships, Alinsunod sa Pagsusuri
-    ng Presidential Fleet https://bit.ly/3O0qSiV'
-  sentences:
-  - 'passage: Happy Birthday ti nakaskasdaaw unay a nanang iti lubong! #panagkasangay
-    #selebrasion'
-  - 'passage: Isang kagubatan na gumagawa ng mga puno ng oak para sa mga materyales
-    sa pagtatayo'
-  - 'passage: Ang pagkadiskaril sa tren miresulta sa daghang mga samad ug kadaot sa
-    palibot nga mga kabtangan.'
-- source_sentence: 'passage: Online and Remote Learning Proves to be Effective During
-    Lockdown'
-  sentences:
-  - 'passage: Gisuspinde sa Ecuador ang rasyon sa kuryente sa pagbalik sa ulan https://t.co/RWCqU0noSq'
-  - 'passage: i feel regretful that i didnt bring overnight gear'
-  - "passage: Creative Dad Has A Delicious Way To Teach His Daughter The ABCs \n"
-- source_sentence: 'passage: Ang pagbibigay ng maling gamot sa isang pasyente na nagreresulta
-    sa mga komplikasyon sa kalusugan'
-  sentences:
-  - 'passage: Rugian ti Iraq dagiti panagregget a mangbangon manen kalpasan ti adu
-    a tawen a gubat'
-  - 'passage: RT @dayurad_: 24 anyos nga Puntland Casino Garowe! @Bulshaawi_ https://t.co/ExWnH7fdW5'
-  - 'passage: PAG-ALAGAD SA PANGKALAHATAG SA GOBYERNO: Libre na ang Flu Shots Anaa
-    na sa Tanang Lokal nga Health Centers'
-- source_sentence: "passage: Girl Does Ice Bucket Challenge... After Having Wisdom\
-    \ Teeth Pulled \n"
-  sentences:
-  - 'passage: New study shows the impact of immigration on social conditions. #ImmigrationImpact'
-  - 'passage: Nabati nako ang akong kaugalingon nga naigo niining katingad-an nga
-    gabon nga bungbong'
-  - 'passage: Just got my child’s educational grading report and couldn’t be more
-    proud of their progress!'
-- source_sentence: "passage: Fit Bodies Aren't Perfect, Either \n"
-  sentences:
-  - 'passage: Royals attend extravagant ceremony to celebrate the opening of new museum'
-  - 'passage: Researchers publish study on the social and psychological impact of
-    online dating'
-  - "passage: Native-American Kids Doused With Beer at SD Hockey Game \n"
-model-index:
-- name: SentenceTransformer based on intfloat/multilingual-e5-large
-  results:
-  - task:
-      type: knowledge-distillation
-      name: Knowledge Distillation
-    dataset:
-      name: Unknown
-      type: unknown
-    metrics:
-    - type: negative_mse
-      value: -0.0055574404541403055
-      name: Negative Mse
----
-
-# SentenceTransformer based on intfloat/multilingual-e5-large
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). It maps sentences & paragraphs to a 1024-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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 1024 tokens
-- **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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 1024, '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
 
@@ -111,7 +17,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
+model = SentenceTransformer("HasinMDG/multilingual-e5-large")
 # Run inference
 sentences = [
     "passage: Fit Bodies Aren't Perfect, Either \n",
@@ -127,436 +33,3 @@ 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
-
-#### Knowledge Distillation
-
-* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
-
-| Metric           | Value       |
-|:-----------------|:------------|
-| **negative_mse** | **-0.0056** |
-
-<!--
-## 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: 22,076 training samples
-* Columns: <code>sentence_0</code> and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                        | label                                 |
-  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------|
-  | type    | string                                                                            | list                                  |
-  | details | <ul><li>min: 7 tokens</li><li>mean: 24.27 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>size: 1024 elements</li></ul> |
-* Samples:
-  | sentence_0                                                                                                                                    | label                                                                                                                             |
-  |:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
-  | <code>passage: Nahibal-an ni Jon Stewart kung kinsa ang modaog sa usa ka gubat tali sa Texas ug Florida <br></code>                           | <code>[0.028687365353107452, -0.017304804176092148, -0.04063289240002632, -0.06607247143983841, 0.012475084513425827, ...]</code> |
-  | <code>passage: Kinabahan si Sarah tungkol sa mga pagsubok at eksaminasyong pang-edukasyon ngunit nagtagumpay silang lahat!</code>             | <code>[0.02698751911520958, -0.04083320125937462, -0.020052699372172356, -0.037999920547008514, 0.025929132476449013, ...]</code> |
-  | <code>passage: (Update)166645: Malinaw na ang obstruction sa N2 Northbound pagkatapos ng Ramp mula sa Umdloti. Mag-ingat sa Pagmaneho.</code> | <code>[0.04197411611676216, -0.017068173736333847, 0.005260208155959845, -0.02268386073410511, 0.016873840242624283, ...]</code>  |
-* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `num_train_epochs`: 20
-- `multi_dataset_batch_sampler`: round_robin
-
-#### 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`: 16
-- `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
-- `num_train_epochs`: 20
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `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`: 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`: 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
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-
-</details>
-
-### Training Logs
-<details><summary>Click to expand</summary>
-
-| Epoch   | Step  | Training Loss | negative_mse |
-|:-------:|:-----:|:-------------:|:------------:|
-| 0.1449  | 200   | -             | -0.0077      |
-| 0.2899  | 400   | -             | -0.0072      |
-| 0.3623  | 500   | 0.0001        | -            |
-| 0.4348  | 600   | -             | -0.0070      |
-| 0.5797  | 800   | -             | -0.0068      |
-| 0.7246  | 1000  | 0.0001        | -0.0067      |
-| 0.8696  | 1200  | -             | -0.0066      |
-| 1.0     | 1380  | -             | -0.0065      |
-| 1.0145  | 1400  | -             | -0.0065      |
-| 1.0870  | 1500  | 0.0           | -            |
-| 1.1594  | 1600  | -             | -0.0064      |
-| 1.3043  | 1800  | -             | -0.0064      |
-| 1.4493  | 2000  | 0.0           | -0.0064      |
-| 1.5942  | 2200  | -             | -0.0063      |
-| 1.7391  | 2400  | -             | -0.0063      |
-| 1.8116  | 2500  | 0.0           | -            |
-| 1.8841  | 2600  | -             | -0.0063      |
-| 2.0     | 2760  | -             | -0.0063      |
-| 2.0290  | 2800  | -             | -0.0063      |
-| 2.1739  | 3000  | 0.0           | -0.0062      |
-| 2.3188  | 3200  | -             | -0.0062      |
-| 2.4638  | 3400  | -             | -0.0061      |
-| 2.5362  | 3500  | 0.0           | -            |
-| 2.6087  | 3600  | -             | -0.0062      |
-| 2.7536  | 3800  | -             | -0.0061      |
-| 2.8986  | 4000  | 0.0           | -0.0061      |
-| 3.0     | 4140  | -             | -0.0061      |
-| 3.0435  | 4200  | -             | -0.0061      |
-| 3.1884  | 4400  | -             | -0.0061      |
-| 3.2609  | 4500  | 0.0           | -            |
-| 3.3333  | 4600  | -             | -0.0061      |
-| 3.4783  | 4800  | -             | -0.0061      |
-| 3.6232  | 5000  | 0.0           | -0.0060      |
-| 3.7681  | 5200  | -             | -0.0060      |
-| 3.9130  | 5400  | -             | -0.0060      |
-| 3.9855  | 5500  | 0.0           | -            |
-| 4.0     | 5520  | -             | -0.0060      |
-| 4.0580  | 5600  | -             | -0.0060      |
-| 4.2029  | 5800  | -             | -0.0060      |
-| 4.3478  | 6000  | 0.0           | -0.0060      |
-| 4.4928  | 6200  | -             | -0.0059      |
-| 4.6377  | 6400  | -             | -0.0059      |
-| 4.7101  | 6500  | 0.0           | -            |
-| 4.7826  | 6600  | -             | -0.0059      |
-| 4.9275  | 6800  | -             | -0.0059      |
-| 5.0     | 6900  | -             | -0.0059      |
-| 5.0725  | 7000  | 0.0           | -0.0059      |
-| 5.2174  | 7200  | -             | -0.0059      |
-| 5.3623  | 7400  | -             | -0.0059      |
-| 5.4348  | 7500  | 0.0           | -            |
-| 5.5072  | 7600  | -             | -0.0059      |
-| 5.6522  | 7800  | -             | -0.0059      |
-| 5.7971  | 8000  | 0.0           | -0.0059      |
-| 5.9420  | 8200  | -             | -0.0059      |
-| 6.0     | 8280  | -             | -0.0058      |
-| 6.0870  | 8400  | -             | -0.0058      |
-| 6.1594  | 8500  | 0.0           | -            |
-| 6.2319  | 8600  | -             | -0.0058      |
-| 6.3768  | 8800  | -             | -0.0059      |
-| 6.5217  | 9000  | 0.0           | -0.0058      |
-| 6.6667  | 9200  | -             | -0.0058      |
-| 6.8116  | 9400  | -             | -0.0058      |
-| 6.8841  | 9500  | 0.0           | -            |
-| 6.9565  | 9600  | -             | -0.0058      |
-| 7.0     | 9660  | -             | -0.0058      |
-| 7.1014  | 9800  | -             | -0.0058      |
-| 7.2464  | 10000 | 0.0           | -0.0058      |
-| 7.3913  | 10200 | -             | -0.0058      |
-| 7.5362  | 10400 | -             | -0.0058      |
-| 7.6087  | 10500 | 0.0           | -            |
-| 7.6812  | 10600 | -             | -0.0058      |
-| 7.8261  | 10800 | -             | -0.0058      |
-| 7.9710  | 11000 | 0.0           | -0.0058      |
-| 8.0     | 11040 | -             | -0.0058      |
-| 8.1159  | 11200 | -             | -0.0057      |
-| 8.2609  | 11400 | -             | -0.0057      |
-| 8.3333  | 11500 | 0.0           | -            |
-| 8.4058  | 11600 | -             | -0.0058      |
-| 8.5507  | 11800 | -             | -0.0058      |
-| 8.6957  | 12000 | 0.0           | -0.0057      |
-| 8.8406  | 12200 | -             | -0.0058      |
-| 8.9855  | 12400 | -             | -0.0057      |
-| 9.0     | 12420 | -             | -0.0057      |
-| 9.0580  | 12500 | 0.0           | -            |
-| 9.1304  | 12600 | -             | -0.0057      |
-| 9.2754  | 12800 | -             | -0.0057      |
-| 9.4203  | 13000 | 0.0           | -0.0057      |
-| 9.5652  | 13200 | -             | -0.0057      |
-| 9.7101  | 13400 | -             | -0.0057      |
-| 9.7826  | 13500 | 0.0           | -            |
-| 9.8551  | 13600 | -             | -0.0057      |
-| 10.0    | 13800 | -             | -0.0057      |
-| 10.1449 | 14000 | 0.0           | -0.0057      |
-| 10.2899 | 14200 | -             | -0.0057      |
-| 10.4348 | 14400 | -             | -0.0057      |
-| 10.5072 | 14500 | 0.0           | -            |
-| 10.5797 | 14600 | -             | -0.0057      |
-| 10.7246 | 14800 | -             | -0.0057      |
-| 10.8696 | 15000 | 0.0           | -0.0057      |
-| 11.0    | 15180 | -             | -0.0057      |
-| 11.0145 | 15200 | -             | -0.0057      |
-| 11.1594 | 15400 | -             | -0.0057      |
-| 11.2319 | 15500 | 0.0           | -            |
-| 11.3043 | 15600 | -             | -0.0057      |
-| 11.4493 | 15800 | -             | -0.0057      |
-| 11.5942 | 16000 | 0.0           | -0.0057      |
-| 11.7391 | 16200 | -             | -0.0056      |
-| 11.8841 | 16400 | -             | -0.0056      |
-| 11.9565 | 16500 | 0.0           | -            |
-| 12.0    | 16560 | -             | -0.0057      |
-| 12.0290 | 16600 | -             | -0.0056      |
-| 12.1739 | 16800 | -             | -0.0056      |
-| 12.3188 | 17000 | 0.0           | -0.0057      |
-| 12.4638 | 17200 | -             | -0.0056      |
-| 12.6087 | 17400 | -             | -0.0056      |
-| 12.6812 | 17500 | 0.0           | -            |
-| 12.7536 | 17600 | -             | -0.0056      |
-| 12.8986 | 17800 | -             | -0.0056      |
-| 13.0    | 17940 | -             | -0.0056      |
-| 13.0435 | 18000 | 0.0           | -0.0056      |
-| 13.1884 | 18200 | -             | -0.0056      |
-| 13.3333 | 18400 | -             | -0.0056      |
-| 13.4058 | 18500 | 0.0           | -            |
-| 13.4783 | 18600 | -             | -0.0056      |
-| 13.6232 | 18800 | -             | -0.0056      |
-| 13.7681 | 19000 | 0.0           | -0.0056      |
-| 13.9130 | 19200 | -             | -0.0056      |
-| 14.0    | 19320 | -             | -0.0056      |
-| 14.0580 | 19400 | -             | -0.0056      |
-| 14.1304 | 19500 | 0.0           | -            |
-| 14.2029 | 19600 | -             | -0.0056      |
-| 14.3478 | 19800 | -             | -0.0056      |
-| 14.4928 | 20000 | 0.0           | -0.0056      |
-| 14.6377 | 20200 | -             | -0.0056      |
-| 14.7826 | 20400 | -             | -0.0056      |
-| 14.8551 | 20500 | 0.0           | -            |
-| 14.9275 | 20600 | -             | -0.0056      |
-| 15.0    | 20700 | -             | -0.0056      |
-| 15.0725 | 20800 | -             | -0.0056      |
-| 15.2174 | 21000 | 0.0           | -0.0056      |
-| 15.3623 | 21200 | -             | -0.0056      |
-| 15.5072 | 21400 | -             | -0.0056      |
-| 15.5797 | 21500 | 0.0           | -            |
-| 15.6522 | 21600 | -             | -0.0056      |
-| 15.7971 | 21800 | -             | -0.0056      |
-| 15.9420 | 22000 | 0.0           | -0.0056      |
-| 16.0    | 22080 | -             | -0.0056      |
-| 16.0870 | 22200 | -             | -0.0056      |
-| 16.2319 | 22400 | -             | -0.0056      |
-| 16.3043 | 22500 | 0.0           | -            |
-| 16.3768 | 22600 | -             | -0.0056      |
-| 16.5217 | 22800 | -             | -0.0056      |
-| 16.6667 | 23000 | 0.0           | -0.0056      |
-| 16.8116 | 23200 | -             | -0.0056      |
-| 16.9565 | 23400 | -             | -0.0056      |
-| 17.0    | 23460 | -             | -0.0056      |
-| 17.0290 | 23500 | 0.0           | -            |
-| 17.1014 | 23600 | -             | -0.0056      |
-| 17.2464 | 23800 | -             | -0.0056      |
-| 17.3913 | 24000 | 0.0           | -0.0056      |
-| 17.5362 | 24200 | -             | -0.0056      |
-| 17.6812 | 24400 | -             | -0.0056      |
-| 17.7536 | 24500 | 0.0           | -            |
-| 17.8261 | 24600 | -             | -0.0056      |
-| 17.9710 | 24800 | -             | -0.0056      |
-| 18.0    | 24840 | -             | -0.0056      |
-| 18.1159 | 25000 | 0.0           | -0.0056      |
-| 18.2609 | 25200 | -             | -0.0056      |
-| 18.4058 | 25400 | -             | -0.0056      |
-| 18.4783 | 25500 | 0.0           | -            |
-| 18.5507 | 25600 | -             | -0.0056      |
-| 18.6957 | 25800 | -             | -0.0056      |
-
-</details>
-
-### Framework Versions
-- Python: 3.10.14
-- Sentence Transformers: 3.1.1
-- Transformers: 4.44.2
-- PyTorch: 2.4.0
-- Accelerate: 0.34.2
-- Datasets: 3.0.0
-- 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",
-}
-```
-
-#### MSELoss
-```bibtex
-@inproceedings{reimers-2020-multilingual-sentence-bert,
-    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2020",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/2004.09813",
-}
-```
-
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