Training in progress, epoch 3, checkpoint

d01ea41 verified 3 months ago

15.7 kB

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:554030
  - loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: pacman smoked turkey
    sentences:
      - omelette with fresh basil & cherry tomatoes
      - mozzarella pacman
      - ' tote '
  - source_sentence: mfk 140 static kite - pulpy
    sentences:
      - kite for young children
      - >-
        leather wrap skirt available in two colors white and black. outside
        materials: leather.
      - >-
        adult long-sleeved thermal football base layer top keepcomfort 100 -
        black
  - source_sentence: large zk diffuser - pack 7
    sentences:
      - ' wrap'
      - zk diffuser
      - leo
  - source_sentence: emerald green double-face drape pajama (short pants)
    sentences:
      - fiber cushion
      - >-
        the double-faced design pajama of the fabric ensures that both sides
        have a glossy finish, providing a stunning look and feel. inside and
        outside material: double face satin
      - sky blue seashell set
  - source_sentence: to - do - dahab
    sentences:
      - >-
        notebook ruled glue binding soft cover 14.2 x 20.8 cm 160 sheets 80 gsm
        leather cover heeton no a25-835
      - ' notebook'
      - ' advance repair lotion'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.9596002101898193
            name: Cosine Accuracy

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("LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine")
# Run inference
sentences = [
    'to - do - dahab',
    ' notebook',
    'notebook ruled glue binding soft cover 14.2 x 20.8 cm 160 sheets 80 gsm leather cover heeton no a25-835',
]
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, 0.3042, 0.2703],
#         [0.3042, 1.0000, 0.7068],
#         [0.2703, 0.7068, 1.0000]])

Evaluation

Metrics

Triplet

Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9596

Training Details

Training Dataset

Unnamed Dataset

Size: 554,030 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 3 tokens
mean: 7.19 tokens
max: 44 tokens

min: 3 tokens
mean: 8.03 tokens
max: 58 tokens
Samples:

anchor positive

grass fed butter basbousa coconut flour basbousa

silver printer tape printer labels

top charcoal tee

	anchor	positive
type	string	string
details	min: 3 tokens mean: 7.19 tokens max: 44 tokens	min: 3 tokens mean: 8.03 tokens max: 58 tokens

anchor	positive
`grass fed butter basbousa`	`coconut flour basbousa`
`silver printer tape`	`printer labels`
`top`	`charcoal tee`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 9,505 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 9.63 tokens max: 43 tokens	min: 3 tokens mean: 6.2 tokens max: 150 tokens	min: 3 tokens mean: 9.58 tokens max: 34 tokens

Samples:

anchor	positive	negative
`pilot mechanical pencil progrex h-127 - 0.7 mm`	`progrex pencil`	`canvas frame 100% cotton 380 gsm 2040 cm rectangular m e5305`
`superior drawing marker -pen - set of 12 colors - 2 nib`	`marker pen`	`blue to-do list`
`first person singular author: haruki murakami`	`first person singular book`	`sesame street 5-minute stories`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 2e-05
weight_decay: 0.001
num_train_epochs: 5
warmup_steps: 1082
fp16: True
dataloader_num_workers: 1
dataloader_prefetch_factor: 2
dataloader_persistent_workers: True
push_to_hub: True
hub_model_id: LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine
hub_strategy: all_checkpoints
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
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: 0.001
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0
warmup_steps: 1082
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: 1
dataloader_prefetch_factor: 2
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: True
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: True
resume_from_checkpoint: None
hub_model_id: LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine
hub_strategy: all_checkpoints
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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy
0.0005	1	3.6183	-	-
0.4619	1000	-	1.2476	0.9518
0.9238	2000	-	1.1979	0.9533
1.0	2165	2.4107	-	-
1.3857	3000	-	1.1493	0.9572
1.8476	4000	-	1.1210	0.9597
2.0	4330	1.8868	-	-
2.3095	5000	-	1.1083	0.9584
2.7714	6000	-	1.0890	0.9596
3.0	6495	1.7202	-	-

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.2
Transformers: 4.53.3
PyTorch: 2.6.0+cu124
Accelerate: 1.9.0
Datasets: 4.4.1
Tokenizers: 0.21.2

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",
}