metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:593
- loss:OnlineContrastiveLoss
widget:
- source_sentence: What city
sentences:
- What magic do other villagers use?
- What does between the gods mean?
- what about the city
- source_sentence: What's your name?
sentences:
- what mystery?
- Is this the flower
- A globe.
- source_sentence: I think we'll find dragons.
sentences:
- Do you know a mage who changes shape of material?
- I don't think we'll find dragons.
- The curtain is moving in the breeze
- source_sentence: What happened to her?
sentences:
- Is this the flower
- Do you have a second bucket?
- There was a red stain on the dish
- source_sentence: I don't see tomato on the shelf
sentences:
- What magic do other villagers use?
- Yes please
- Because the pot smelled spicy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: custom arc semantics data en
type: custom-arc-semantics-data-en
metrics:
- type: cosine_accuracy
value: 0.9495798319327731
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.6676459908485413
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6361173391342163
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9
name: Cosine Precision
- type: cosine_recall
value: 0.9
name: Cosine Recall
- type: cosine_ap
value: 0.8400025542161988
name: Cosine Ap
- type: dot_accuracy
value: 0.9495798319327731
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.6676459908485413
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9
name: Dot F1
- type: dot_f1_threshold
value: 0.6361173391342163
name: Dot F1 Threshold
- type: dot_precision
value: 0.9
name: Dot Precision
- type: dot_recall
value: 0.9
name: Dot Recall
- type: dot_ap
value: 0.8400025542161988
name: Dot Ap
- type: manhattan_accuracy
value: 0.9495798319327731
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 12.677780151367188
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.896551724137931
name: Manhattan F1
- type: manhattan_f1_threshold
value: 12.677780151367188
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.9285714285714286
name: Manhattan Precision
- type: manhattan_recall
value: 0.8666666666666667
name: Manhattan Recall
- type: manhattan_ap
value: 0.8387174899512584
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9495798319327731
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.8152118921279907
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.8530915379524231
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.9
name: Euclidean Precision
- type: euclidean_recall
value: 0.9
name: Euclidean Recall
- type: euclidean_ap
value: 0.8400025542161988
name: Euclidean Ap
- type: max_accuracy
value: 0.9495798319327731
name: Max Accuracy
- type: max_accuracy_threshold
value: 12.677780151367188
name: Max Accuracy Threshold
- type: max_f1
value: 0.9
name: Max F1
- type: max_f1_threshold
value: 12.677780151367188
name: Max F1 Threshold
- type: max_precision
value: 0.9285714285714286
name: Max Precision
- type: max_recall
value: 0.9
name: Max Recall
- type: max_ap
value: 0.8400025542161988
name: Max Ap
SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the csv dataset. 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 tokens
- 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': 256, 'do_lower_case': False}) with Transformer model: 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("LeoChiuu/all-MiniLM-L6-v2-arc")
# Run inference
sentences = [
"I don't see tomato on the shelf",
'Because the pot smelled spicy',
'What magic do other villagers use?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Binary Classification
- Dataset:
custom-arc-semantics-data-en
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9496 |
cosine_accuracy_threshold | 0.6676 |
cosine_f1 | 0.9 |
cosine_f1_threshold | 0.6361 |
cosine_precision | 0.9 |
cosine_recall | 0.9 |
cosine_ap | 0.84 |
dot_accuracy | 0.9496 |
dot_accuracy_threshold | 0.6676 |
dot_f1 | 0.9 |
dot_f1_threshold | 0.6361 |
dot_precision | 0.9 |
dot_recall | 0.9 |
dot_ap | 0.84 |
manhattan_accuracy | 0.9496 |
manhattan_accuracy_threshold | 12.6778 |
manhattan_f1 | 0.8966 |
manhattan_f1_threshold | 12.6778 |
manhattan_precision | 0.9286 |
manhattan_recall | 0.8667 |
manhattan_ap | 0.8387 |
euclidean_accuracy | 0.9496 |
euclidean_accuracy_threshold | 0.8152 |
euclidean_f1 | 0.9 |
euclidean_f1_threshold | 0.8531 |
euclidean_precision | 0.9 |
euclidean_recall | 0.9 |
euclidean_ap | 0.84 |
max_accuracy | 0.9496 |
max_accuracy_threshold | 12.6778 |
max_f1 | 0.9 |
max_f1_threshold | 12.6778 |
max_precision | 0.9286 |
max_recall | 0.9 |
max_ap | 0.84 |
Training Details
Training Dataset
csv
- Dataset: csv
- Size: 593 training samples
- Columns:
text1
,text2
, andlabel
- Approximate statistics based on the first 593 samples:
text1 text2 label type string string int details - min: 3 tokens
- mean: 7.2 tokens
- max: 18 tokens
- min: 3 tokens
- mean: 6.8 tokens
- max: 18 tokens
- 0: ~68.14%
- 1: ~31.86%
- Samples:
text1 text2 label Something is different
What did you say?
0
what are the properties?
what about Jack?
0
hint
hints
1
- Loss:
OnlineContrastiveLoss
Evaluation Dataset
csv
- Dataset: csv
- Size: 593 evaluation samples
- Columns:
text1
,text2
, andlabel
- Approximate statistics based on the first 593 samples:
text1 text2 label type string string int details - min: 3 tokens
- mean: 7.26 tokens
- max: 13 tokens
- min: 3 tokens
- mean: 7.13 tokens
- max: 13 tokens
- 0: ~74.79%
- 1: ~25.21%
- Samples:
text1 text2 label To have an adventure with us
Its name is Oblivion.
0
Is the scarf on the nightstand?
Are you using my slippers?
0
To test Unravel Spell
Tell me about Lily
0
- Loss:
OnlineContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochlearning_rate
: 2e-05num_train_epochs
: 13warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 13max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | custom-arc-semantics-data-en_max_ap |
---|---|---|---|---|
None | 0 | - | - | 0.7634 |
1.0 | 60 | 0.3053 | 0.1297 | 0.7825 |
2.0 | 120 | 0.1478 | 0.1071 | 0.8071 |
3.0 | 180 | 0.0357 | 0.0904 | 0.8387 |
4.0 | 240 | 0.0139 | 0.0829 | 0.8412 |
5.0 | 300 | 0.017 | 0.0704 | 0.8429 |
6.0 | 360 | 0.0132 | 0.0779 | 0.8411 |
7.0 | 420 | 0.0 | 0.0700 | 0.8433 |
8.0 | 480 | 0.0079 | 0.0808 | 0.8403 |
9.0 | 540 | 0.0098 | 0.0808 | 0.8404 |
10.0 | 600 | 0.0039 | 0.0804 | 0.8387 |
11.0 | 660 | 0.0001 | 0.0815 | 0.8398 |
12.0 | 720 | 0.0039 | 0.0816 | 0.8397 |
13.0 | 780 | 0.0034 | 0.0814 | 0.8400 |
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.20.0
- Tokenizers: 0.19.1
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",
}