SentenceTransformer based on srikarvar/fine_tuned_model_5
This is a sentence-transformers model finetuned from srikarvar/fine_tuned_model_5 on the json 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: srikarvar/fine_tuned_model_5
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
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: 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("srikarvar/fine_tuned_model_13")
# Run inference
sentences = [
'This framework has a strong connection with cloud platforms, making it simple to deploy and share models with the developer community.',
'Yes, the framework is deeply integrated with cloud-based platforms, allowing for easy deployment and sharing with the developer community.',
'UserRole data is properly converted to arrays.',
]
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
Information Retrieval
- Dataset:
e5-cogcache-small-refined
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 1.0 |
cosine_accuracy@3 | 1.0 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 1.0 |
cosine_precision@3 | 0.3333 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 1.0 |
cosine_recall@3 | 1.0 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 1.0 |
cosine_mrr@10 | 1.0 |
cosine_map@100 | 1.0 |
dot_accuracy@1 | 1.0 |
dot_accuracy@3 | 1.0 |
dot_accuracy@5 | 1.0 |
dot_accuracy@10 | 1.0 |
dot_precision@1 | 1.0 |
dot_precision@3 | 0.3333 |
dot_precision@5 | 0.2 |
dot_precision@10 | 0.1 |
dot_recall@1 | 1.0 |
dot_recall@3 | 1.0 |
dot_recall@5 | 1.0 |
dot_recall@10 | 1.0 |
dot_ndcg@10 | 1.0 |
dot_mrr@10 | 1.0 |
dot_map@100 | 1.0 |
Information Retrieval
- Dataset:
e5-cogcache-small-refined
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 1.0 |
cosine_accuracy@3 | 1.0 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 1.0 |
cosine_precision@3 | 0.3333 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 1.0 |
cosine_recall@3 | 1.0 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 1.0 |
cosine_mrr@10 | 1.0 |
cosine_map@100 | 1.0 |
dot_accuracy@1 | 1.0 |
dot_accuracy@3 | 1.0 |
dot_accuracy@5 | 1.0 |
dot_accuracy@10 | 1.0 |
dot_precision@1 | 1.0 |
dot_precision@3 | 0.3333 |
dot_precision@5 | 0.2 |
dot_precision@10 | 0.1 |
dot_recall@1 | 1.0 |
dot_recall@3 | 1.0 |
dot_recall@5 | 1.0 |
dot_recall@10 | 1.0 |
dot_ndcg@10 | 1.0 |
dot_mrr@10 | 1.0 |
dot_map@100 | 1.0 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 560 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 560 samples:
anchor positive type string string details - min: 9 tokens
- mean: 30.18 tokens
- max: 98 tokens
- min: 8 tokens
- mean: 30.0 tokens
- max: 98 tokens
- Samples:
anchor positive It is not available in v2.10.0.
No, it doesn't exist in v2.10.0.
You can become a member of the research forum and pose questions to the AI community.
You can join and ask questions in the AI research forum.
No information regarding initializing a project for PyTorch is included in the guide.
The guide does not provide information on how to initialize a project for PyTorch.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 16per_device_eval_batch_size
: 16warmup_ratio
: 0.1batch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3max_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
: Falsefp16_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
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | e5-cogcache-small-refined_cosine_map@100 |
---|---|---|---|
0 | 0 | - | 0.9911 |
0.3125 | 10 | 0.0088 | - |
0.625 | 20 | 0.001 | - |
0.9375 | 30 | 0.0064 | - |
1.0 | 32 | - | 1.0 |
1.25 | 40 | 0.0 | - |
1.5625 | 50 | 0.0001 | - |
1.875 | 60 | 0.0002 | - |
2.0 | 64 | - | 1.0 |
2.1875 | 70 | 0.0003 | - |
2.5 | 80 | 0.0001 | - |
2.8125 | 90 | 0.0002 | - |
3.0 | 96 | - | 1.0 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- 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",
}
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}
}
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Model tree for srikarvar/fine_tuned_model_13
Base model
intfloat/multilingual-e5-small
Finetuned
srikarvar/fine_tuned_model_5
Evaluation results
- Cosine Accuracy@1 on e5 cogcache small refinedself-reported1.000
- Cosine Accuracy@3 on e5 cogcache small refinedself-reported1.000
- Cosine Accuracy@5 on e5 cogcache small refinedself-reported1.000
- Cosine Accuracy@10 on e5 cogcache small refinedself-reported1.000
- Cosine Precision@1 on e5 cogcache small refinedself-reported1.000
- Cosine Precision@3 on e5 cogcache small refinedself-reported0.333
- Cosine Precision@5 on e5 cogcache small refinedself-reported0.200
- Cosine Precision@10 on e5 cogcache small refinedself-reported0.100
- Cosine Recall@1 on e5 cogcache small refinedself-reported1.000
- Cosine Recall@3 on e5 cogcache small refinedself-reported1.000