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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("srikarvar/fine_tuned_model_12")
# Run inference
sentences = [
    'The `num_services` method gives the quantity of services in the garage.',
    'The `num_services` method returns the number of services in the garage.',
    'It returns the number of entries in the dataset.',
]
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: pair-class-dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9821
cosine_accuracy_threshold	0.9923
cosine_f1	0.991
cosine_f1_threshold	0.9923
cosine_precision	1.0
cosine_recall	0.9821
cosine_ap	1.0
dot_accuracy	0.9821
dot_accuracy_threshold	0.9923
dot_f1	0.991
dot_f1_threshold	0.9923
dot_precision	1.0
dot_recall	0.9821
dot_ap	1.0
manhattan_accuracy	0.9821
manhattan_accuracy_threshold	1.8806
manhattan_f1	0.991
manhattan_f1_threshold	1.8806
manhattan_precision	1.0
manhattan_recall	0.9821
manhattan_ap	1.0
euclidean_accuracy	0.9821
euclidean_accuracy_threshold	0.1216
euclidean_f1	0.991
euclidean_f1_threshold	0.1216
euclidean_precision	1.0
euclidean_recall	0.9821
euclidean_ap	1.0
max_accuracy	0.9821
max_accuracy_threshold	1.8806
max_f1	0.991
max_f1_threshold	1.8806
max_precision	1.0
max_recall	0.9821
max_ap	1.0

Binary Classification

Dataset: pair-class-test
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9821
cosine_accuracy_threshold	0.9923
cosine_f1	0.991
cosine_f1_threshold	0.9923
cosine_precision	1.0
cosine_recall	0.9821
cosine_ap	1.0
dot_accuracy	0.9821
dot_accuracy_threshold	0.9923
dot_f1	0.991
dot_f1_threshold	0.9923
dot_precision	1.0
dot_recall	0.9821
dot_ap	1.0
manhattan_accuracy	0.9821
manhattan_accuracy_threshold	1.8806
manhattan_f1	0.991
manhattan_f1_threshold	1.8806
manhattan_precision	1.0
manhattan_recall	0.9821
manhattan_ap	1.0
euclidean_accuracy	0.9821
euclidean_accuracy_threshold	0.1216
euclidean_f1	0.991
euclidean_f1_threshold	0.1216
euclidean_precision	1.0
euclidean_recall	0.9821
euclidean_ap	1.0
max_accuracy	0.9821
max_accuracy_threshold	1.8806
max_f1	0.991
max_f1_threshold	1.8806
max_precision	1.0
max_recall	0.9821
max_ap	1.0

Training Details

Training Dataset

json

Dataset: json
Size: 560 training samples
Columns: label, sentence2, and sentence1
Approximate statistics based on the first 560 samples:
label sentence2 sentence1
type int string string
details
1: 100.00%
min: 9 tokens
mean: 30.18 tokens
max: 98 tokens
min: 8 tokens
mean: 30.0 tokens
max: 98 tokens

Samples:

label	sentence2	sentence1
`1`	`It is not available in v2.10.0.`	`No, it doesn't exist in v2.10.0.`
`1`	`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.`
`1`	`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: OnlineContrastiveLoss

Evaluation Dataset

json

Dataset: json
Size: 560 evaluation samples
Columns: label, sentence2, and sentence1
Approximate statistics based on the first 560 samples:
label sentence2 sentence1
type int string string
details
1: 100.00%
min: 15 tokens
mean: 32.29 tokens
max: 82 tokens
min: 14 tokens
mean: 31.96 tokens
max: 82 tokens

Samples:

label	sentence2	sentence1
`1`	`The how-to guides for the platform include instructions for Setup, Processing, Streaming, TensorFlow integration, PyTorch integration, Caching, Cloud storage, Indexing, Analytics, and Data Pipelines.`	`The how-to guides for the platform include Setup, Processing, Streaming, TensorFlow integration, PyTorch integration, Cache management, Cloud storage, Search index, Analytics, and Data Pipelines.`
`1`	`In the absence of a model script, all files in the supported formats will be loaded. However, if a model script is present, it will be downloaded and executed in order to download and prepare the model.`	`If there’s no model script, all the files in the supported formats are loaded. If there’s a model script, it is downloaded and executed to download and prepare the model.`
`1`	`React, Angular, and Vue are compatible with the Plugin library.`	`The Plugin library can be used with React, Angular, and Vue.`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
gradient_accumulation_steps: 2
num_train_epochs: 4
warmup_ratio: 0.1
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_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: 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_fused
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	pair-class-dev_max_ap	pair-class-test_max_ap
0	0	-	-	1.0	-
1.0	8	-	0.0028	1.0	-
1.25	10	0.1425	-	-	-
2.0	16	-	0.0003	1.0	-
2.5	20	0.002	-	-	-
3.0	24	-	0.0001	1.0	-
3.75	30	0.0008	-	-	-
4.0	32	-	0.0001	1.0	1.0

The bold row denotes the saved checkpoint.

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

bibtex

@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",
}

	label	sentence2	sentence1
type	int	string	string
details	1: 100.00%	min: 9 tokens mean: 30.18 tokens max: 98 tokens	min: 8 tokens mean: 30.0 tokens max: 98 tokens