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base_model: sentence-transformers/all-MiniLM-L6-v2
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:2244
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- loss:MultipleNegativesRankingLoss
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sentences:
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- source_sentence: Who
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sentences:
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-->
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---
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base_model: sentence-transformers/all-MiniLM-L6-v2
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:2244
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- loss:MultipleNegativesRankingLoss
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- Education
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- Retrieval
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- Syllabus
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widget:
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- source_sentence: Participation in both lecture and discussion sections is required.
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sentences:
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- >-
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Service learning: 1.5 to 2 units depending on portfolio evaluation. Must
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meet 15 service hours for max credit.
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- >-
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Students are expected to attend both lectures and discussion sessions for
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full participation credit.
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- 'General info mailbox: replies from Dr. Anil Goyal and Prof. Lucy Salgado.'
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- source_sentence: What is the name of the TA?
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sentences:
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- The instructors on record are Prof. Jae Sun Kim and Dr. Bea Valdez.
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- TAs include Priya Reddy and Lena Hoffmann.
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- 'The official hours: 4 credits.'
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- source_sentence: Who are the teaching staff?
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- 'Teaching staff: Dr. Doris Ren, Prof. David Sarpong, Prof. Olivia Boland.'
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- 'Teaching faculty: Dr. Malak Mahfouz and Prof. William Ruiz.'
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- 'Lab teaching assistants: Julio Mendez, Siri Eriksson.'
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- source_sentence: Who operates the Canvas FAQ as TA?
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- 'FAQ moderators: Pia Fenwick, Pilar Nasser. Email [email protected].'
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- 'Designation: 3 credit hours.'
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- 'Lab: 1 hour (semester).'
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- source_sentence: What is the eligibility requirement to register for extra unit load?
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sentences:
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- Completion awards three credit hours.
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- 'Eligibility for extra: GPA >= 3.5 and at least 30 completed units.'
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- 'The academic credit hours: 2'
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---
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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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.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
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- **Maximum Sequence Length:** 256 tokens
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- **Output Dimensionality:** 384 dimensions
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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(2): Normalize()
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("rsajja/Fine-tuned-Educational-Model-MNRL")
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# Run inference
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sentences = [
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'What is the eligibility requirement to register for extra unit load?',
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'Eligibility for extra: GPA >= 3.5 and at least 30 completed units.',
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'Completion awards three credit hours.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 384]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*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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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Dataset
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#### Unnamed Dataset
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* Size: 2,244 training samples
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* Columns: <code>sentence_0</code> and <code>sentence_1</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence_0 | sentence_1 |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
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| type | string | string |
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| details | <ul><li>min: 4 tokens</li><li>mean: 9.79 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.6 tokens</li><li>max: 98 tokens</li></ul> |
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* Samples:
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| sentence_0 | sentence_1 |
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|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|
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| <code>Students with disabilities may request accommodations.</code> | <code>Accessibility services are available to students when needed.</code> |
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| <code>Who teaches the course?</code> | <code>This course is taught collaboratively by Dr. Louise McCann and Dr. Omar Franco.</code> |
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| <code>State the credit hour load for this section.</code> | <code>Credit load: 3.5 hours</code> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `per_device_train_batch_size`: 64
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- `per_device_eval_batch_size`: 64
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- `num_train_epochs`: 25
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- `multi_dataset_batch_sampler`: round_robin
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: no
|
188 |
+
- `prediction_loss_only`: True
|
189 |
+
- `per_device_train_batch_size`: 64
|
190 |
+
- `per_device_eval_batch_size`: 64
|
191 |
+
- `per_gpu_train_batch_size`: None
|
192 |
+
- `per_gpu_eval_batch_size`: None
|
193 |
+
- `gradient_accumulation_steps`: 1
|
194 |
+
- `eval_accumulation_steps`: None
|
195 |
+
- `torch_empty_cache_steps`: None
|
196 |
+
- `learning_rate`: 5e-05
|
197 |
+
- `weight_decay`: 0.0
|
198 |
+
- `adam_beta1`: 0.9
|
199 |
+
- `adam_beta2`: 0.999
|
200 |
+
- `adam_epsilon`: 1e-08
|
201 |
+
- `max_grad_norm`: 1
|
202 |
+
- `num_train_epochs`: 25
|
203 |
+
- `max_steps`: -1
|
204 |
+
- `lr_scheduler_type`: linear
|
205 |
+
- `lr_scheduler_kwargs`: {}
|
206 |
+
- `warmup_ratio`: 0.0
|
207 |
+
- `warmup_steps`: 0
|
208 |
+
- `log_level`: passive
|
209 |
+
- `log_level_replica`: warning
|
210 |
+
- `log_on_each_node`: True
|
211 |
+
- `logging_nan_inf_filter`: True
|
212 |
+
- `save_safetensors`: True
|
213 |
+
- `save_on_each_node`: False
|
214 |
+
- `save_only_model`: False
|
215 |
+
- `restore_callback_states_from_checkpoint`: False
|
216 |
+
- `no_cuda`: False
|
217 |
+
- `use_cpu`: False
|
218 |
+
- `use_mps_device`: False
|
219 |
+
- `seed`: 42
|
220 |
+
- `data_seed`: None
|
221 |
+
- `jit_mode_eval`: False
|
222 |
+
- `use_ipex`: False
|
223 |
+
- `bf16`: False
|
224 |
+
- `fp16`: False
|
225 |
+
- `fp16_opt_level`: O1
|
226 |
+
- `half_precision_backend`: auto
|
227 |
+
- `bf16_full_eval`: False
|
228 |
+
- `fp16_full_eval`: False
|
229 |
+
- `tf32`: None
|
230 |
+
- `local_rank`: 0
|
231 |
+
- `ddp_backend`: None
|
232 |
+
- `tpu_num_cores`: None
|
233 |
+
- `tpu_metrics_debug`: False
|
234 |
+
- `debug`: []
|
235 |
+
- `dataloader_drop_last`: False
|
236 |
+
- `dataloader_num_workers`: 0
|
237 |
+
- `dataloader_prefetch_factor`: None
|
238 |
+
- `past_index`: -1
|
239 |
+
- `disable_tqdm`: False
|
240 |
+
- `remove_unused_columns`: True
|
241 |
+
- `label_names`: None
|
242 |
+
- `load_best_model_at_end`: False
|
243 |
+
- `ignore_data_skip`: False
|
244 |
+
- `fsdp`: []
|
245 |
+
- `fsdp_min_num_params`: 0
|
246 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
247 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
248 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
249 |
+
- `deepspeed`: None
|
250 |
+
- `label_smoothing_factor`: 0.0
|
251 |
+
- `optim`: adamw_torch
|
252 |
+
- `optim_args`: None
|
253 |
+
- `adafactor`: False
|
254 |
+
- `group_by_length`: False
|
255 |
+
- `length_column_name`: length
|
256 |
+
- `ddp_find_unused_parameters`: None
|
257 |
+
- `ddp_bucket_cap_mb`: None
|
258 |
+
- `ddp_broadcast_buffers`: False
|
259 |
+
- `dataloader_pin_memory`: True
|
260 |
+
- `dataloader_persistent_workers`: False
|
261 |
+
- `skip_memory_metrics`: True
|
262 |
+
- `use_legacy_prediction_loop`: False
|
263 |
+
- `push_to_hub`: False
|
264 |
+
- `resume_from_checkpoint`: None
|
265 |
+
- `hub_model_id`: None
|
266 |
+
- `hub_strategy`: every_save
|
267 |
+
- `hub_private_repo`: False
|
268 |
+
- `hub_always_push`: False
|
269 |
+
- `gradient_checkpointing`: False
|
270 |
+
- `gradient_checkpointing_kwargs`: None
|
271 |
+
- `include_inputs_for_metrics`: False
|
272 |
+
- `eval_do_concat_batches`: True
|
273 |
+
- `fp16_backend`: auto
|
274 |
+
- `push_to_hub_model_id`: None
|
275 |
+
- `push_to_hub_organization`: None
|
276 |
+
- `mp_parameters`:
|
277 |
+
- `auto_find_batch_size`: False
|
278 |
+
- `full_determinism`: False
|
279 |
+
- `torchdynamo`: None
|
280 |
+
- `ray_scope`: last
|
281 |
+
- `ddp_timeout`: 1800
|
282 |
+
- `torch_compile`: False
|
283 |
+
- `torch_compile_backend`: None
|
284 |
+
- `torch_compile_mode`: None
|
285 |
+
- `dispatch_batches`: None
|
286 |
+
- `split_batches`: None
|
287 |
+
- `include_tokens_per_second`: False
|
288 |
+
- `include_num_input_tokens_seen`: False
|
289 |
+
- `neftune_noise_alpha`: None
|
290 |
+
- `optim_target_modules`: None
|
291 |
+
- `batch_eval_metrics`: False
|
292 |
+
- `eval_on_start`: False
|
293 |
+
- `use_liger_kernel`: False
|
294 |
+
- `eval_use_gather_object`: False
|
295 |
+
- `prompts`: None
|
296 |
+
- `batch_sampler`: batch_sampler
|
297 |
+
- `multi_dataset_batch_sampler`: round_robin
|
298 |
+
|
299 |
+
</details>
|
300 |
+
|
301 |
+
### Training Logs
|
302 |
+
| Epoch | Step | Training Loss |
|
303 |
+
|:-------:|:----:|:-------------:|
|
304 |
+
| 13.8889 | 500 | 0.5206 |
|
305 |
+
|
306 |
+
|
307 |
+
### Framework Versions
|
308 |
+
- Python: 3.9.13
|
309 |
+
- Sentence Transformers: 4.1.0
|
310 |
+
- Transformers: 4.45.1
|
311 |
+
- PyTorch: 2.0.1+cpu
|
312 |
+
- Accelerate: 0.34.2
|
313 |
+
- Datasets: 3.0.1
|
314 |
+
- Tokenizers: 0.20.0
|
315 |
+
|
316 |
+
## Citation
|
317 |
+
|
318 |
+
### BibTeX
|
319 |
+
|
320 |
+
#### Sentence Transformers
|
321 |
+
```bibtex
|
322 |
+
@inproceedings{reimers-2019-sentence-bert,
|
323 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
324 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
325 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
326 |
+
month = "11",
|
327 |
+
year = "2019",
|
328 |
+
publisher = "Association for Computational Linguistics",
|
329 |
+
url = "https://arxiv.org/abs/1908.10084",
|
330 |
+
}
|
331 |
+
```
|
332 |
+
|
333 |
+
#### MultipleNegativesRankingLoss
|
334 |
+
```bibtex
|
335 |
+
@misc{henderson2017efficient,
|
336 |
+
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
337 |
+
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},
|
338 |
+
year={2017},
|
339 |
+
eprint={1705.00652},
|
340 |
+
archivePrefix={arXiv},
|
341 |
+
primaryClass={cs.CL}
|
342 |
+
}
|
343 |
+
```
|
344 |
+
|
345 |
+
<!--
|
346 |
+
## Glossary
|
347 |
+
|
348 |
+
*Clearly define terms in order to be accessible across audiences.*
|
349 |
+
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|
350 |
+
|
351 |
+
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|
352 |
+
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|
353 |
+
|
354 |
+
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|
355 |
+
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|
356 |
+
|
357 |
+
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|
358 |
+
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|
359 |
+
|
360 |
+
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|
361 |
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