checkpoint-203/README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:456
  - loss:SoftmaxLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: not especially natural
    sentences:
      - bright
      - bright
      - bright
  - source_sentence: くつろいだ感じじゃない
    sentences:
      - bright
      - bright
      - cozy
  - source_sentence: not especially bright
    sentences:
      - bright
      - cozy
      - natural
  - source_sentence: 明るくしないで
    sentences:
      - cozy
      - cozy
      - bright
  - source_sentence: This room feels too cozy I need something more energetic
    sentences:
      - cozy
      - bright
      - bright
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2
• Maximum Sequence Length: 128 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': 128, '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})
)

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
sentences = [
    'This room feels too cozy I need something more energetic',
    'bright',
    'cozy',
]
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]

Training Details

Training Dataset

Unnamed Dataset

• Size: 456 training samples
• Columns: premise, hypothesis, and label
• Approximate statistics based on the first 456 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	min: 4 tokens mean: 7.82 tokens max: 20 tokens	min: 3 tokens mean: 3.31 tokens max: 4 tokens	0: ~17.98% 1: ~82.02%

• Samples:

  premise	hypothesis	label
  not romantic lighting	bright	1
  These lights are way too bright please turn them down	cozy	1
  not quite cozy	bright	1

• Loss: SoftmaxLoss

Evaluation Dataset

Unnamed Dataset

• Size: 115 evaluation samples
• Columns: premise, hypothesis, and label
• Approximate statistics based on the first 115 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	min: 3 tokens mean: 7.24 tokens max: 17 tokens	min: 3 tokens mean: 3.38 tokens max: 4 tokens	0: ~20.87% 1: ~79.13%

• Samples:

  premise	hypothesis	label
  not warm	cozy	0
  In the evening I want lighting that's not bright but cozy	cozy	1
  明るい光は苦手です	cozy	1

• Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 32
• learning_rate: 2e-05
• num_train_epochs: 7
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• ddp_find_unused_parameters: False

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 16
• 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: 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: 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: True
• 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: False
• 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
0.1724	5	0.7808	-
0.3448	10	0.7224	-
0.5172	15	0.5833	-
0.6897	20	0.4336	-
0.8621	25	0.426	-
1.0	29	-	0.4209
1.0345	30	0.407	-
1.2069	35	0.4633	-
1.3793	40	0.2629	-
1.5517	45	0.4468	-
1.7241	50	0.3665	-
1.8966	55	0.2735	-
2.0	58	-	0.3269
2.0690	60	0.2472	-
2.2414	65	0.2586	-
2.4138	70	0.2281	-
2.5862	75	0.3056	-
2.7586	80	0.2166	-
2.9310	85	0.2243	-
3.0	87	-	0.2471
3.1034	90	0.2233	-
3.2759	95	0.1625	-
3.4483	100	0.1718	-
3.6207	105	0.1728	-
3.7931	110	0.1949	-
3.9655	115	0.0891	-
4.0	116	-	0.1997
4.1379	120	0.1895	-
4.3103	125	0.1021	-
4.4828	130	0.1232	-
4.6552	135	0.0891	-
4.8276	140	0.109	-
5.0	145	0.0879	0.1679
5.1724	150	0.0814	-
5.3448	155	0.1015	-
5.5172	160	0.0822	-
5.6897	165	0.1054	-
5.8621	170	0.1093	-
6.0	174	-	0.1479
6.0345	175	0.0911	-
6.2069	180	0.0804	-
6.3793	185	0.1063	-
6.5517	190	0.0821	-
6.7241	195	0.0988	-
6.8966	200	0.0691	-
7.0	203	-	0.1430

Framework Versions

• Python: 3.10.16
• Sentence Transformers: 3.3.1
• Transformers: 4.48.0
• PyTorch: 2.4.0
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}