distilbert-base-uncased-sts / README.md

Pyro-X2

Add new SentenceTransformer model

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:4480
  - loss:CosineSimilarityLoss
base_model: distilbert/distilbert-base-uncased
widget:
  - source_sentence: I have the same thing.
    sentences:
      - And, Obama gets zero credit for the budget under him.
      - UK urges countries over Syria aid
      - I have the same situation and have traveled extensively.
  - source_sentence: a man wearing a gray hat fishing out of a fishing boat.
    sentences:
      - A man wearing a straw hat and fishing vest in a stream.
      - no, it's not an answer.
      - Mann's work and the HS was all about Tree rings.
  - source_sentence: A small white cat with glowing eyes standing underneath a chair.
    sentences:
      - A white cat stands on the floor.
      - A woman is cutting a tomato.
      - The man is playing the piano with his nose.
  - source_sentence: Originally Posted by muslim girl ooops sorry!
    sentences:
      - Originally Posted by muslim girl its not a complete impossibility.
      - A person riding a dirt bike.
      - >-
        None of the casualties was Americans, said Capt. Michael Calvert,
        regiment spokesman.
  - source_sentence: Tell us what the charges were.
    sentences:
      - The Judges orders a three-page letter to be filed.
      - Yes what are his charges.
      - A person is buttering a tray.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on distilbert/distilbert-base-uncased
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.3779858984516553
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.473144636361867
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.34896468808057485
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.44906241393019836
            name: Spearman Cosine

SentenceTransformer based on distilbert/distilbert-base-uncased

This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased on the csv dataset. 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/distilbert-base-uncased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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:

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("Pyro-X2/distilbert-base-uncased-sts")
# Run inference
sentences = [
    'Tell us what the charges were.',
    'Yes what are his charges.',
    'A person is buttering a tray.',
]
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]

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.378	0.349
spearman_cosine	0.4731	0.4491

Training Details

Training Dataset

csv

Dataset: csv
Size: 4,480 training samples
Columns: sentence1, sentence2, and score

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	score
type	string	string	int
details	min: 6 tokens mean: 15.14 tokens max: 50 tokens	min: 6 tokens mean: 15.07 tokens max: 52 tokens	0: ~14.20% 1: ~11.60% 2: ~18.40% 3: ~23.30% 4: ~21.70% 5: ~10.80%

Samples:

sentence1	sentence2	score
`A man is speaking.`	`A man is spitting.`	`1`
`Austrian found hoarding 56 stolen skulls in home museum`	`Austrian man charged after 56 human skulls are found at his home`	`4`
`Mitt Romney wins Republican primary in Indiana`	`Romney wins Florida Republican primary`	`2`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

csv

Dataset: csv
Size: 560 evaluation samples
Columns: sentence1, sentence2, and score

Approximate statistics based on the first 560 samples:

	sentence1	sentence2	score
type	string	string	int
details	min: 5 tokens mean: 14.41 tokens max: 44 tokens	min: 5 tokens mean: 14.28 tokens max: 42 tokens	0: ~12.86% 1: ~16.96% 2: ~14.82% 3: ~18.21% 4: ~26.43% 5: ~10.71%

Samples:

sentence1	sentence2	score
`An airplane is flying in the air.`	`A South African Airways plane is flying in a blue sky.`	`3`
`A television, upholstered chair, and coffee stable in a bright room.`	`A leather couch and wooden table in a living room.`	`2`
`Red panda‚Äôs short-lived zoo escape`	`India‚Äôs march to Mars`	`0`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
warmup_ratio: 0.1

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 1.0
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
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: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0.3571	100	5.031	5.0990	0.4973	-
0.7143	200	4.9152	5.0985	0.4944	-
1.0714	300	4.8198	5.0984	0.4959	-
1.4286	400	4.9102	5.0983	0.4884	-
1.7857	500	4.9238	5.0983	0.4798	-
2.1429	600	4.9387	5.0983	0.4777	-
2.5	700	4.8955	5.0983	0.4752	-
2.8571	800	4.9623	5.0983	0.4740	-
3.2143	900	4.7754	5.0983	0.4739	-
3.5714	1000	4.936	5.0983	0.4734	-
3.9286	1100	4.9254	5.0983	0.4731	-
-1	-1	-	-	-	0.4491

Framework Versions

Python: 3.12.12
Sentence Transformers: 4.1.0
Transformers: 4.49.0
PyTorch: 2.3.0.post101
Accelerate: 1.10.1
Datasets: 3.3.2
Tokenizers: 0.21.0

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",
}