rsajja
/

Fine-tuned-Educational-Model-Dual-Loss

@@ -1,381 +1,387 @@
----
-base_model: sentence-transformers/all-MiniLM-L6-v2
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:4828
-- loss:MultipleNegativesRankingLoss
-- loss:CosineSimilarityLoss
-widget:
-- source_sentence: What is the credit hour load?
-  sentences:
-  - 'Received: 4 credit hours.'
-  - 'Part-time: typically 6–8 credits per term, this class is 2.'
-  - 'Credit hour load: 1.5 hours.'
-- source_sentence: Who has authority for final grade requests?
-  sentences:
-  - What is the attendance policy?
-  - 'Audit code: R, always equivalent to 0 credit, but fees may still apply.'
-  - 'Grade authority: Prof. Sun-Jung Kim, Dr. Avery McGregor, Dr. Fahad Jameel (jameel@northarea.edu).
-    All appeals must be emailed.'
-- source_sentence: Who is the secondary TA?
-  sentences:
-  - 'Secondary TAs: Lena Müller and Chayan Biswas.'
-  - 'Supervising faculty: Prof. Taylor Moya, Dr. Seung Ahn.'
-  - 'Credits per class: 3.'
-- source_sentence: Who is the grading TA?
-  sentences:
-  - 'Weekly pollsters: Dr. Anna Massoud, Prof. Darius Okoye.'
-  - 'Course assistants this term: Ximena Michel and Seth Downey.'
-  - 'Grading TAs: Miho Saito and Victor Ortiz.'
-- source_sentence: Who will support main lecture slide posting as TA?
-  sentences:
-  - 'Slide uploads: Brittany Stewart and Daniel Simmel.'
-  - 'This course''s teachers: Dr. Paula Gillespie and Dr. Nikola Petrovic.'
-  - Canvas communications are handled equally by Ewan McLean (ewanm@coll.edu), Maryse
-    Rodrigues, and Soonjin Ko (skoon@coll.edu). Use '@TA' to get a quick response.
----
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
-- **Maximum Sequence Length:** 256 tokens
-- **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
-  (1): Pooling({'word_embedding_dimension': 384, '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("rsajja/Fine-tuned-Educational-Model-Dual-Loss")
-# Run inference
-sentences = [
-    'Who will support main lecture slide posting as TA?',
-    'Slide uploads: Brittany Stewart and Daniel Simmel.',
-    "This course's teachers: Dr. Paula Gillespie and Dr. Nikola Petrovic.",
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 384]
-# 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.*
--->
-<!--
-## 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 Datasets
-#### Unnamed Dataset
-* Size: 2,193 training samples
-* Columns: <code>sentence_0</code> and <code>sentence_1</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                       | sentence_1                                                                        |
-  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                           | string                                                                            |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 9.79 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.04 tokens</li><li>max: 95 tokens</li></ul> |
-* Samples:
-  | sentence_0                                      | sentence_1                                                                |
-  |:------------------------------------------------|:--------------------------------------------------------------------------|
-  | <code>Who teaches this section?</code>          | <code>Section teachers: Dr. Sandy Rivera and Dr. Ludovic Tremblay.</code> |
-  | <code>How many hours of academic credit?</code> | <code>This course grants 2 academic hours.</code>                         |
-  | <code>Who guides this class?</code>             | <code>Class guides: Dr. Marina Sokolov, Dr. Andre Williams.</code>        |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 20.0,
-      "similarity_fct": "cos_sim"
-  }
-  ```
-#### Unnamed Dataset
-* Size: 2,635 training samples
-* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                       | sentence_1                                                                        | label                                                          |
-  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                           | string                                                                            | float                                                          |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 9.72 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.62 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.84</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence_0                                                    | sentence_1                                                                       | label            |
-  |:--------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------|
-  | <code>Which TAs are supporting the class?</code>              | <code>Supporting TAs: Steve Bernstein, Rina Nanji.</code>                        | <code>1.0</code> |
-  | <code>For how many credit hours?</code>                       | <code>The course is for 6 credit hours.</code>                                   | <code>1.0</code> |
-  | <code>Who are the teaching assistants for this course?</code> | <code>This course's teaching assistants include Troy Kim and Deborah Lee.</code> | <code>1.0</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
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
-- `num_train_epochs`: 25
-- `multi_dataset_batch_sampler`: round_robin
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
-- `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`: 25
-- `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
-- `use_liger_kernel`: False
-- `eval_use_gather_object`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-</details>
-### Training Logs
-| Epoch   | Step | Training Loss |
-|:-------:|:----:|:-------------:|
-| 7.1429  | 500  | 0.3755        |
-| 14.2857 | 1000 | 0.169         |
-| 21.4286 | 1500 | 0.1504        |
-### Framework Versions
-- Python: 3.9.13
-- Sentence Transformers: 4.1.0
-- Transformers: 4.45.1
-- PyTorch: 2.0.1+cpu
-- Accelerate: 0.34.2
-- Datasets: 3.0.1
-- Tokenizers: 0.20.0
-## 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}
-}
-```
-<!--
-## 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.*
 -->

+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:4828
+- loss:MultipleNegativesRankingLoss
+- loss:CosineSimilarityLoss
+- Education
+- Retrieval
+- Syllabus
+widget:
+- source_sentence: What is the credit hour load?
+  sentences:
+  - 'Received: 4 credit hours.'
+  - 'Part-time: typically 6–8 credits per term, this class is 2.'
+  - 'Credit hour load: 1.5 hours.'
+- source_sentence: Who has authority for final grade requests?
+  sentences:
+  - What is the attendance policy?
+  - 'Audit code: R, always equivalent to 0 credit, but fees may still apply.'
+  - >-
+    Grade authority: Prof. Sun-Jung Kim, Dr. Avery McGregor, Dr. Fahad Jameel
+    (jameel@northarea.edu). All appeals must be emailed.
+- source_sentence: Who is the secondary TA?
+  sentences:
+  - 'Secondary TAs: Lena Müller and Chayan Biswas.'
+  - 'Supervising faculty: Prof. Taylor Moya, Dr. Seung Ahn.'
+  - 'Credits per class: 3.'
+- source_sentence: Who is the grading TA?
+  sentences:
+  - 'Weekly pollsters: Dr. Anna Massoud, Prof. Darius Okoye.'
+  - 'Course assistants this term: Ximena Michel and Seth Downey.'
+  - 'Grading TAs: Miho Saito and Victor Ortiz.'
+- source_sentence: Who will support main lecture slide posting as TA?
+  sentences:
+  - 'Slide uploads: Brittany Stewart and Daniel Simmel.'
+  - 'This course''s teachers: Dr. Paula Gillespie and Dr. Nikola Petrovic.'
+  - >-
+    Canvas communications are handled equally by Ewan McLean (ewanm@coll.edu),
+    Maryse Rodrigues, and Soonjin Ko (skoon@coll.edu). Use '@TA' to get a quick
+    response.
+---
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
+  (1): Pooling({'word_embedding_dimension': 384, '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("rsajja/Fine-tuned-Educational-Model-Dual-Loss")
+# Run inference
+sentences = [
+    'Who will support main lecture slide posting as TA?',
+    'Slide uploads: Brittany Stewart and Daniel Simmel.',
+    "This course's teachers: Dr. Paula Gillespie and Dr. Nikola Petrovic.",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+# 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.*
+-->
+<!--
+## 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 Datasets
+#### Unnamed Dataset
+* Size: 2,193 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                        |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.79 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.04 tokens</li><li>max: 95 tokens</li></ul> |
+* Samples:
+  | sentence_0                                      | sentence_1                                                                |
+  |:------------------------------------------------|:--------------------------------------------------------------------------|
+  | <code>Who teaches this section?</code>          | <code>Section teachers: Dr. Sandy Rivera and Dr. Ludovic Tremblay.</code> |
+  | <code>How many hours of academic credit?</code> | <code>This course grants 2 academic hours.</code>                         |
+  | <code>Who guides this class?</code>             | <code>Class guides: Dr. Marina Sokolov, Dr. Andre Williams.</code>        |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+#### Unnamed Dataset
+* Size: 2,635 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                        | label                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.72 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.62 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.84</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                    | sentence_1                                                                       | label            |
+  |:--------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------|
+  | <code>Which TAs are supporting the class?</code>              | <code>Supporting TAs: Steve Bernstein, Rina Nanji.</code>                        | <code>1.0</code> |
+  | <code>For how many credit hours?</code>                       | <code>The course is for 6 credit hours.</code>                                   | <code>1.0</code> |
+  | <code>Who are the teaching assistants for this course?</code> | <code>This course's teaching assistants include Troy Kim and Deborah Lee.</code> | <code>1.0</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
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 25
+- `multi_dataset_batch_sampler`: round_robin
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 25
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+</details>
+### Training Logs
+| Epoch   | Step | Training Loss |
+|:-------:|:----:|:-------------:|
+| 7.1429  | 500  | 0.3755        |
+| 14.2857 | 1000 | 0.169         |
+| 21.4286 | 1500 | 0.1504        |
+### Framework Versions
+- Python: 3.9.13
+- Sentence Transformers: 4.1.0
+- Transformers: 4.45.1
+- PyTorch: 2.0.1+cpu
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.20.0
+## 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}
+}
+```
+<!--
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