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gnlp_hw1_encoder

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:4893
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
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gnlp_hw1_encoder / README.md
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4893
- loss:TripletLoss
base_model: distilbert/distilroberta-base
widget:
- source_sentence: Leave me alone! Have you gone daft? Mister Spock needs me! Let
go! That will be quite enough. Thank you, doctor.; Please, release her.[SEP]What's
this all about?
sentences:
- ' You know, the lab here, they have a paid intern position. It''s usually given
to one of the kids from the universities but, if you want, I could pRobably get
you an interview. There''s some entry lEvel stuff, some gofer work. But you''d
also have access to a lot of cool things.'
- She was doing as I requested, Mister Scott. A Vulcan form of self-healing.
- Thasians have been referred to in our records as having the power to transmute
objects or render substances invisible. It has generally been regarded as legend,
but Charlie does seems to possess this same power.
- source_sentence: Why would you do this? Because the needs of the one ...outweigh
the needs of the many. I have been ...and ever shall be ...your friend. Yes! Yes,
Spock. The ship. ...Out of danger?[SEP]You saved the ship, ...You saved us all.
Don't you remember?
sentences:
- ' My wife had taken a sleeping pill and gone to bed. It was Christmas Eve. Kyle
popped corn in the fireplace. He Managed to knock loose some tinder. Wrapping
paper caught on fire. Spread so fast. I got Kyle outta there. When I went back
in for... [Chokes, takes a beat, then.]'
- In two days, you'll have your own hands, Thalassa. Mechanically efficient and
quite human-looking. Android robot hands, of course. Hands without feeling. Enjoy
the taste of life while you can.
- Jim, ...your name is Jim.
- source_sentence: Captain, if something hasn't worked out and therefore has no scientific
fact Shall we leave it up to the doctor? Since you brought me down here for advice,
Captain One of the advantages of being a Captain, Doctor, is being able to ask
for advice without necessarily having to take it. I think I'll have to award that
round to the Captain, Helen. You're fighting over your weight. All right, let's
take a look.[SEP]I'm not a criminal! I do not require neural neutraliser.
sentences:
- Neural neutraliser. Can you explain that, Doctor Van Gelder?
- ' And the disorientation?'
- I'm aware of these facts. Please get on with the job. Computer.
- source_sentence: We're picking up an object, sir. Much larger, coming toward us.
Coming. Exceptionally strong contact. Not visual yet. Distant spectrograph. Metallic,
similar to cube. Much greater energy reading. There, sir. Half speed. Prepare
for evasive action.[SEP]Reducing to warp two, sir.
sentences:
- Tractor beam, Captain. Something's grabbed us, hard.
- Exactly.
- ' There''s a blockage in the urinary tract. Simple terms, your baby can''t pee.
His bladder is swollen and it''s crushing his lungs.'
- source_sentence: My father says you have been my friend. ...You came back for me.
You would have done the same for me. Why would you do this? Because the needs
of the one ...outweigh the needs of the many. I have been ...and ever shall be
...your friend.[SEP]Yes! Yes, Spock.
sentences:
- But a defensible entrance, Captain.
- ' No, blood tests were all normal. And he clotted in six minutes.'
- The ship. ...Out of danger?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
results:
- task:
type: triplet
name: Triplet
dataset:
name: evaluator enc
type: evaluator_enc
metrics:
- type: cosine_accuracy
value: 0.9989781379699707
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: evaluator val
type: evaluator_val
metrics:
- type: cosine_accuracy
value: 0.9872685074806213
name: Cosine Accuracy
---
# SentenceTransformer based on distilbert/distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base). 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 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': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(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("greatakela/gnlp_hw1_encoder")
# Run inference
sentences = [
'My father says you have been my friend. ...You came back for me. You would have done the same for me. Why would you do this? Because the needs of the one ...outweigh the needs of the many. I have been ...and ever shall be ...your friend.[SEP]Yes! Yes, Spock.',
'The ship. ...Out of danger?',
' No, blood tests were all normal. And he clotted in six minutes.',
]
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]
```
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### Direct Usage (Transformers)
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Triplet
* Datasets: `evaluator_enc` and `evaluator_val`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | evaluator_enc | evaluator_val |
|:--------------------|:--------------|:--------------|
| **cosine_accuracy** | **0.999** | **0.9873** |
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 4,893 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 83.38 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.38 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.48 tokens</li><li>max: 102 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The usage is correct. The creator was simply testing your memory banks. There was much damage in the accident. Mister Singh. Come here a moment. This unit will see to your needs. Sir? I'll be back in a moment. Gentlemen, come with me.[SEP]You're on to something, Spock. What is it?</code> | <code>I've correlated all the available information on the Nomad probe, and I'm convinced that this object is indeed that probe.</code> | <code> DIC would explain both the!</code> |
| <code>Mister Spock, how many people are on Memory Alpha? It varies with the number of scholars, researchers, and scientists from the various Federation planets who are using the computer complex. Captain, we are within orbit range. Lock into orbit. Aye, sir.[SEP]It is leaving Memory Alpha, Captain.</code> | <code>Sensors give no readings of generated energy from Memory Alpha, Captain.</code> | <code> Weird huh?</code> |
| <code>We're guiding around most of the time ripples now. Mister Spock? All plotted but one, Captain. Coming up on it now. Seems to be fairly heavy displacement. Bones! Get back to your positions. The hypo, Captain.[SEP]It was set for cordrazine.</code> | <code>Empty.</code> | <code> Actually he's only in the Navy when they sang, In The Navy. The rest of the time he's just in generic fatigues. [House stares at him.] What? You brought it up! [House starts to walk out.] You didn't flush.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `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`: 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
- `num_train_epochs`: 3
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | evaluator_enc_cosine_accuracy | evaluator_val_cosine_accuracy |
|:------:|:----:|:-------------:|:-----------------------------:|:-----------------------------:|
| -1 | -1 | - | 0.5866 | - |
| 0.4902 | 300 | - | 0.9875 | - |
| 0.8170 | 500 | 1.085 | - | - |
| 0.9804 | 600 | - | 0.9935 | - |
| 1.0 | 612 | - | 0.9937 | - |
| 1.4706 | 900 | - | 0.9967 | - |
| 1.6340 | 1000 | 0.1573 | - | - |
| 1.9608 | 1200 | - | 0.9980 | - |
| 2.0 | 1224 | - | 0.9980 | - |
| 2.4510 | 1500 | 0.0733 | 0.9990 | - |
| 2.9412 | 1800 | - | 0.9990 | - |
| 3.0 | 1836 | - | 0.9990 | - |
| -1 | -1 | - | - | 0.9873 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- 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",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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