finetuned-jetbrains-cosqa

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from 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 <!-- 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
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'how do i check if a string is in a list in python',
    'def isin(elems, line):\n    """Check if an element from a list is in a string.\n\n    :type elems: list\n    :type line: str\n\n    """\n    found = False\n    for e in elems:\n        if e in line.lower():\n            found = True\n            break\n    return found',
    'def dedup_list(l):\n    """Given a list (l) will removing duplicates from the list,\n       preserving the original order of the list. Assumes that\n       the list entrie are hashable."""\n    dedup = set()\n    return [ x for x in l if not (x in dedup or dedup.add(x))]',
]
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, 0.8394, 0.2006],
#         [0.8394, 1.0000, 0.1654],
#         [0.2006, 0.1654, 1.0000]])

<!-- ### 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 Dataset

Unnamed Dataset

• Size: 19,604 training samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 9.57 tokens</li><li>max: 21 tokens</li></ul>	<ul><li>min: 36 tokens</li><li>mean: 83.77 tokens</li><li>max: 256 tokens</li></ul>	<ul><li>min: 39 tokens</li><li>mean: 86.33 tokens</li><li>max: 256 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>python code to write bool value 1</code>	<code>def writeBoolean(self, n):<br> """<br> Writes a Boolean to the stream.<br> """<br> t = TYPE_BOOL_TRUE<br><br> if n is False:<br> t = TYPE_BOOL_FALSE<br><br> self.stream.write(t)</code>	<code>def cmd_dot(conf: Config):<br> """Print out a neat targets dependency tree based on requested targets.<br><br> Use graphviz to render the dot file, e.g.:<br><br> > ybt dot :foo :bar | dot -Tpng -o graph.png<br> """<br> build_context = BuildContext(conf)<br> populate_targets_graph(build_context, conf)<br> if conf.output_dot_file is None:<br> write_dot(build_context, conf, sys.stdout)<br> else:<br> with open(conf.output_dot_file, 'w') as out_file:<br> write_dot(build_context, conf, out_file)</code>
  <code>"python how to manipulate clipboard"</code>	<code>def paste(xsel=False):<br> """Returns system clipboard contents."""<br> selection = "primary" if xsel else "clipboard"<br> try:<br> return subprocess.Popen(["xclip", "-selection", selection, "-o"], stdout=subprocess.PIPE).communicate()[0].decode("utf-8")<br> except OSError as why:<br> raise XclipNotFound</code>	<code>def log_request(self, code='-', size='-'):<br> """Selectively log an accepted request."""<br><br> if self.server.logRequests:<br> BaseHTTPServer.BaseHTTPRequestHandler.log_request(self, code, size)</code>
  <code>python colored output to html</code>	<code>def _format_json(data, theme):<br> """Pretty print a dict as a JSON, with colors if pygments is present."""<br> output = json.dumps(data, indent=2, sort_keys=True)<br><br> if pygments and sys.stdout.isatty():<br> style = get_style_by_name(theme)<br> formatter = Terminal256Formatter(style=style)<br> return pygments.highlight(output, JsonLexer(), formatter)<br><br> return output</code>	<code>def average(arr):<br> """average of the values, must have more than 0 entries.<br><br> :param arr: list of numbers<br> :type arr: number[] a number array<br> :return: average<br> :rtype: float<br><br> """<br> if len(arr) == 0:<br> sys.stderr.write("ERROR: no content in array to take average\n")<br> sys.exit()<br> if len(arr) == 1: return arr[0]<br> return float(sum(arr))/float(len(arr))</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• learning_rate: 1e-05
• num_train_epochs: 2
• warmup_steps: 1000
• router_mapping: {'texts': {'0': 'anchor', '1': 'positive', '2': 'negative'}}

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: 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: 1e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 1000
• 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
• 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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: no
• 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: True
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {'texts': {'0': 'anchor', '1': 'positive', '2': 'negative'}}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.1.1
• Transformers: 4.57.1
• PyTorch: 2.9.0+cu128
• Accelerate: 1.10.1
• Datasets: 3.6.0
• Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@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

@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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