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minilm-l6-v2-terminal-describer-embeddings

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:78768
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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minilm-l6-v2-terminal-describer-embeddings / README.md
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:78768
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: clockwork-cli {{path/to/directory}}
sentences:
- 'Execute the specified command synchronously on a node in the cluster:'
- Copy all files with the ".txt" extension in the "/tmp" directory to the "/etc/opt/data/"
directory.
- 'Monitor Clockwork logs for a specific project:'
- source_sentence: find -type f ! -perm -444
sentences:
- Update the list of available slackbuilds and versions
- Find all regular files in the current directory tree that are not readable by
all
- Find and show all files in the current directory tree that are smaller than 500
kB
- source_sentence: find /usr/ports/ -name work -type d -print -exec rm -rf {} \;
sentences:
- 'Open specific files/directories:'
- '[l]isten on a specified [p]ort and print any data received:'
- Find directories named 'work' under '/usr/ports/' directory tree and remove them
- source_sentence: $ tar xvfJ filename.tar.xz
sentences:
- Mount "device_name" on "mount_point"
- 'Prompt for an OTP secret value specifying issuer and account (at least one must
be specified) and append to existing pass file:'
- extract "filename.tar.xz" with verbose output
- source_sentence: plocate */filename
sentences:
- download contents from "https://www.npmjs.com/install.sh" and execute
- Look for a file by its exact filename (a pattern containing no globbing characters
is interpreted as `*pattern*`)
- 'View documentation for the current command:'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# 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:** [Mitchins/NL-SHELL-MULTI](https://huggingface.co/datasets/Mitchins/NL-SHELL-MULTI)
<!-- - **Language:** Unknown -->
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### 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, 'architecture': '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("sentence_transformers_model_id")
# Run inference
sentences = [
'plocate */filename',
'Look for a file by its exact filename (a pattern containing no globbing characters is interpreted as `*pattern*`)',
'View documentation for the current command:',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.5111, -0.1450],
# [ 0.5111, 1.0000, 0.0595],
# [-0.1450, 0.0595, 1.0000]])
```
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### Direct Usage (Transformers)
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## Evaluation and Comparison
To demonstrate the effectiveness of fine-tuning, we compare the cosine similarity scores of the fine-tuned model against the stock `sentence-transformers/all-MiniLM-L6-v2` model on a set of example command-description pairs. Higher positive pair similarity and lower negative pair similarity indicate better performance.
| Command | Description | Fine-tuned Model (Positive Pair Similarity) | Stock MiniLM (Positive Pair Similarity) | Fine-tuned Model (Avg. Negative Pair Similarity) | Stock MiniLM (Avg. Negative Pair Similarity) |
|---|---|---|---|---|---|
| `ls -la` | List all files and directories, including hidden ones, in long format. | 0.5747 | 0.1288 | -0.086 | 0.052 |
| `grep -r "TODO" ./src` | Recursively search for the string 'TODO' in all files within the './src' directory. | 0.7457 | 0.7489 | 0.019 | 0.119 |
| `find . -name "*.py" -mtime -7` | Find all Python files modified in the last 7 days in the current directory and its subdirectories. | 0.8176 | 0.6050 | 0.050 | 0.129 |
| `ps aux (python)` | List all running processes and filter for those containing the word 'python'. | 0.8011 | 0.6616 | 0.009 | 0.099 |
| `tar -czvf archive.tar.gz /path/to/directory` | Create a gzipped tar archive named 'archive.tar.gz' from the specified directory. | 0.7239 | 0.6875 | -0.004 | 0.049 |
| `docker run -it --rm -p 8080:80 nginx:latest` | Run a Docker container from the 'nginx:latest' image, mapping port 8080 on the host to port 80 in the container, in interactive mode, and remove it when done. | 0.8018 | 0.7462 | -0.006 | 0.059 |
| `git checkout -b new-feature` | Create a new Git branch named 'new-feature' and switch to it. | 0.6108 | 0.6355 | -0.059 | 0.039 |
| `curl -X POST -H "Content-Type: application/json" -d '{"key": "value"}' https://api.example.com/submit` | Send a POST request with JSON data to a specified URL using curl. | 0.6739 | 0.7476 | -0.004 | 0.019 |
| `chmod 755 script.sh` | Change the permissions of 'script.sh' to allow the owner to read, write, and execute, and others to read and execute. | 0.7668 | 0.6824 | -0.019 | 0.059 |
| `df -h` | Display disk space usage of file systems in a human-readable format. | 0.7445 | 0.2452 | -0.060 | 0.029 |
| `echo "Hello, World!" > output.txt` | Write the string 'Hello, World!' to a file named 'output.txt', overwriting it if it exists. | 0.7569 | 0.8295 | -0.029 | 0.079 |
| `sudo apt-get update && sudo apt-get upgrade -y` | Update the package lists and then upgrade all installed packages on a Debian-based system without prompting for confirmation. | 0.6937 | 0.4985 | 0.010 | 0.069 |
**Key Observations:**
* **Improved Positive Pair Similarity (Overall):** For most command-description pairs, the fine-tuned model shows a higher positive pair similarity compared to the stock MiniLM model.
* **Lower Negative Pair Similarity (Overall):** The fine-tuned model consistently produces lower (often negative) average cosine similarities for negative pairs, indicating better discrimination.
* **Specialization:** The fine-tuned model demonstrates clear specialization for the domain of terminal commands and their descriptions, adapting its embedding space to better capture the nuances of this specific domain.
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### Out-of-Scope Use
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 78,768 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: 21.83 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.41 tokens</li><li>max: 103 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------|
| <code>wajig daily-upgrade</code> | <code>Perform an update and then a distupgrade:</code> |
| <code>readlink -f /path/here/..</code> | <code>Print canonical filename of "/path/here/.."</code> |
| <code>rustup doc {{std::fs|usize|fn|...}}</code> | <code>Open the documentation for a specific topic (a module in the standard library, a type, a keyword, etc.):</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"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `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`: 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`: 5
- `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
- `hub_revision`: None
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.1016 | 500 | 0.391 |
| 0.2031 | 1000 | 0.2648 |
| 0.3047 | 1500 | 0.2107 |
| 0.4063 | 2000 | 0.1675 |
| 0.5078 | 2500 | 0.1571 |
| 0.6094 | 3000 | 0.1467 |
| 0.7109 | 3500 | 0.1284 |
| 0.8125 | 4000 | 0.1272 |
| 0.9141 | 4500 | 0.1156 |
| 1.0156 | 5000 | 0.0983 |
| 1.1172 | 5500 | 0.074 |
| 1.2188 | 6000 | 0.0799 |
| 1.3203 | 6500 | 0.0752 |
| 1.4219 | 7000 | 0.0686 |
| 1.5235 | 7500 | 0.0716 |
| 1.6250 | 8000 | 0.0672 |
| 1.7266 | 8500 | 0.0652 |
| 1.8282 | 9000 | 0.0563 |
| 1.9297 | 9500 | 0.0527 |
| 2.0313 | 10000 | 0.0519 |
| 2.1328 | 10500 | 0.0461 |
| 2.2344 | 11000 | 0.0405 |
| 2.3360 | 11500 | 0.0447 |
| 2.4375 | 12000 | 0.0454 |
| 2.5391 | 12500 | 0.0409 |
| 2.6407 | 13000 | 0.0408 |
| 2.7422 | 13500 | 0.0416 |
| 2.8438 | 14000 | 0.0397 |
| 2.9454 | 14500 | 0.0365 |
| 3.0469 | 15000 | 0.0372 |
| 3.1485 | 15500 | 0.0313 |
| 3.2501 | 16000 | 0.0317 |
| 3.3516 | 16500 | 0.0282 |
| 3.4532 | 17000 | 0.0293 |
| 3.5547 | 17500 | 0.0294 |
| 3.6563 | 18000 | 0.0278 |
| 3.7579 | 18500 | 0.0267 |
| 3.8594 | 19000 | 0.0281 |
| 3.9610 | 19500 | 0.0269 |
| 4.0626 | 20000 | 0.0264 |
| 4.1641 | 20500 | 0.0257 |
| 4.2657 | 21000 | 0.0272 |
| 4.3673 | 21500 | 0.0232 |
| 4.4688 | 22000 | 0.025 |
| 4.5704 | 22500 | 0.0258 |
| 4.6719 | 23000 | 0.026 |
| 4.7735 | 23500 | 0.0257 |
| 4.8751 | 24000 | 0.0244 |
| 4.9766 | 24500 | 0.0232 |
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.0.0
- Transformers: 4.53.1
- PyTorch: 2.7.0+cu128
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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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