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

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("dimickkatarina/st-finetuned-cosqa")
# Run inference
sentences = [
    'bottom 5 rows in python',
    'def table_top_abs(self):\n        """Returns the absolute position of table top"""\n        table_height = np.array([0, 0, self.table_full_size[2]])\n        return string_to_array(self.floor.get("pos")) + table_height',
    'def refresh(self, document):\n\t\t""" Load a new copy of a document from the database.  does not\n\t\t\treplace the old one """\n\t\ttry:\n\t\t\told_cache_size = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\t\treturn obj',
]
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,  0.5112, -0.0912],
#         [ 0.5112,  1.0000, -0.0497],
#         [-0.0912, -0.0497,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 9,020 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 6 tokens
mean: 9.67 tokens
max: 21 tokens

min: 40 tokens
mean: 86.17 tokens
max: 256 tokens

	anchor	positive
type	string	string
details	min: 6 tokens mean: 9.67 tokens max: 21 tokens	min: 40 tokens mean: 86.17 tokens max: 256 tokens

Samples:

anchor	positive
`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)`
`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`
`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]`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 4
warmup_ratio: 0.1
dataloader_drop_last: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 64
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: 2e-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: 4
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
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: True
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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0714	10	0.3791
0.1429	20	0.3626
0.2143	30	0.2615
0.2857	40	0.3141
0.3571	50	0.2518
0.4286	60	0.2634
0.5	70	0.2632
0.5714	80	0.2297
0.6429	90	0.2026
0.7143	100	0.2151
0.7857	110	0.2281
0.8571	120	0.1605
0.9286	130	0.1922
1.0	140	0.1857
1.0714	150	0.1826
1.1429	160	0.2142
1.2143	170	0.1679
1.2857	180	0.1761
1.3571	190	0.2048
1.4286	200	0.1439
1.5	210	0.1792
1.5714	220	0.1937
1.6429	230	0.1797
1.7143	240	0.1374
1.7857	250	0.1577
1.8571	260	0.1594
1.9286	270	0.1582
2.0	280	0.1464
2.0714	290	0.1189
2.1429	300	0.1257
2.2143	310	0.1501
2.2857	320	0.1827
2.3571	330	0.1559
2.4286	340	0.1348
2.5	350	0.1298
2.5714	360	0.1445
2.6429	370	0.1417
2.7143	380	0.1865
2.7857	390	0.1613
2.8571	400	0.1433
2.9286	410	0.1402
3.0	420	0.1409
3.0714	430	0.1219
3.1429	440	0.1423
3.2143	450	0.1426
3.2857	460	0.1336
3.3571	470	0.1163
3.4286	480	0.1122
3.5	490	0.153
3.5714	500	0.1288
3.6429	510	0.1352
3.7143	520	0.1629
3.7857	530	0.1074
3.8571	540	0.149
3.9286	550	0.1315
4.0	560	0.1227

Framework Versions

Python: 3.12.6
Sentence Transformers: 5.1.1
Transformers: 4.57.0
PyTorch: 2.8.0+cpu
Accelerate: 1.10.1
Datasets: 4.2.0
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}
}