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DornierDo17
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all-MiniLM-L6-v2-CosQA

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:9020
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
xet
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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
  • Maximum Sequence Length: 256 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

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
queries = [
    "check if address is valid python",
]
documents = [
    'def is_valid_ipv6(ip_str):\n    """\n    Check the validity of an IPv6 address\n    """\n    try:\n        socket.inet_pton(socket.AF_INET6, ip_str)\n    except socket.error:\n        return False\n    return True',
    'def longest_run_1d(arr):\n    """Return the length of the longest consecutive run of identical values.\n\n    Parameters\n    ----------\n    arr : bool array\n      Input array\n\n    Returns\n    -------\n    int\n      Length of longest run.\n    """\n    v, rl = rle_1d(arr)[:2]\n    return np.where(v, rl, 0).max()',
    'def dict_to_enum_fn(d: Dict[str, Any], enum_class: Type[Enum]) -> Enum:\n    """\n    Converts an ``dict`` to a ``Enum``.\n    """\n    return enum_class[d[\'name\']]',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.6137, -0.0250, -0.1585]])

Training Details

Training Dataset

Unnamed Dataset

  • Size: 9,020 training samples
  • Columns: query, document, and score
  • Approximate statistics based on the first 1000 samples:
    query document score
    type string string int
    details
    • min: 6 tokens
    • mean: 9.67 tokens
    • max: 21 tokens
    • min: 40 tokens
    • mean: 86.17 tokens
    • max: 256 tokens
    • 1: 100.00%
  • Samples:
    query document score
    1d array in char datatype in python def _convert_to_array(array_like, dtype):
    """
    Convert Matrix attributes which are array-like or buffer to array.
    """
    if isinstance(array_like, bytes):
    return np.frombuffer(array_like, dtype=dtype)
    return np.asarray(array_like, dtype=dtype)    		 1
    python condition non none def _not(condition=None, **kwargs):
    """
    Return the opposite of input condition.

    :param condition: condition to process.

    :result: not condition.
    :rtype: bool
    """

    result = True

    if condition is not None:
    result = not run(condition, **kwargs)

    return result    		 1
    accessing a column from a matrix in python def get_column(self, X, column):
    """Return a column of the given matrix.

    Args:
    X: numpy.ndarray or pandas.DataFrame.
    column: int or str.

    Returns:
    np.ndarray: Selected column.
    """
    if isinstance(X, pd.DataFrame):
    return X[column].values

    return X[:, column]    		 1
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Evaluation Dataset

Unnamed Dataset

  • Size: 500 evaluation samples
  • Columns: query, document, and score
  • Approximate statistics based on the first 500 samples:
    query document score
    type string string int
    details
    • min: 6 tokens
    • mean: 9.71 tokens
    • max: 21 tokens
    • min: 42 tokens
    • mean: 87.19 tokens
    • max: 256 tokens
    • 1: 100.00%
  • Samples:
    query document score
    python check if a variable is iterable def is_iterable_but_not_string(obj):
    """
    Determine whether or not obj is iterable but not a string (eg, a list, set, tuple etc).
    """
    return hasattr(obj, 'iter') and not isinstance(obj, str) and not isinstance(obj, bytes)    		 1
    how to prevent a file from modifying python def make_file_readable (filename):
    """Make file user readable if it is not a link."""
    if not os.path.islink(filename):
    util.set_mode(filename, stat.S_IRUSR)    		 1
    how to make functions that returns a function python def apply(filter):
    """Manufacture decorator that filters return value with given function.

    filter:
    Callable that takes a single parameter.
    """
    def decorator(callable):
    return lambda *args, **kwargs: filter(callable(*args, **kwargs))
    return decorator    		 1
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • num_train_epochs: 7
  • warmup_ratio: 0.1
  • fp16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: epoch
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • 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: 1.0
  • num_train_epochs: 7
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • 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: True
  • 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
  • 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: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Epoch Step Training Loss Validation Loss
0.7092 100 0.2775 -
1.0 141 - 0.1604
1.4184 200 0.1842 -
2.0 282 - 0.1411
2.1277 300 0.1482 -
2.8369 400 0.1164 -
3.0 423 - 0.1278
3.5461 500 0.1073 -
4.0 564 - 0.1177
4.2553 600 0.0936 -
4.9645 700 0.0831 -
5.0 705 - 0.1114
5.6738 800 0.0748 -
6.0 846 - 0.1078
6.3830 900 0.0746 -

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.1.1
  • Transformers: 4.56.2
  • PyTorch: 2.8.0+cu128
  • Accelerate: 1.10.1
  • Datasets: 4.1.1
  • 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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