This is a sentence-transformers model trained. It maps sentences & paragraphs to a 1024-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

Maximum Sequence Length: 256 tokens
Output Dimensionality: 1024 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': 'RobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    ' still has a lot of food fears, incredibly picky regardless and lots of food anxiety\\* this is important',
    ' My relationship with food was much different in high school, I would eat without even thinking about it',
    " I've been trying to keep down breakfast and supper everyday plus some snacks and I don't think it's enough but I don't know if I'd be able to recover if I was eating any more than that",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6203, 0.5744],
#         [0.6203, 1.0000, 0.7722],
#         [0.5744, 0.7722, 1.0000]])

Evaluation

Metrics

Binary Classification

Dataset: quora_duplicates_dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.7276
cosine_accuracy_threshold	0.6382
cosine_f1	0.6511
cosine_f1_threshold	0.3571
cosine_precision	0.5738
cosine_recall	0.7524
cosine_ap	0.7166
cosine_mcc	0.3529

Training Details

Training Dataset

Unnamed Dataset

Size: 6,000 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 4 tokens
mean: 33.52 tokens
max: 169 tokens
min: 5 tokens
mean: 32.67 tokens
max: 228 tokens
0: ~57.10%
1: ~42.90%

Samples:

sentence1	sentence2	label
`Obviously as my age is specified there's not much I can do from a physical standpoint other than cook for her when I'm with her, but I'e been there for her emotionally in the past for other things and I think it helped her`	`any advice would be greatly appreciated to help her`	`1`
`I am 15 years old and recently I have been feeling awful about it and realized how much of a problem it has become`	`[object Object][object Object]And I know the answer is probably, "go see a doctor or therapist/talk to ur parents" however I'm worried about doing that since what if it's nothing but me worrying and making a deal about nothing, I don't want them judging me over something potentially stupid like this`	`0`
`This is Part 1 of 2 for 1 post) A friend asked me for some advice for her sister who is currently struggling with her relationship to food and her body`	`[object Object][object Object]The things I'm concerned about:[object Object]- I don't like eating, I try to avoid it until my stomach physically hurts, especially when I have a major depressive episode`	`1`

Loss: ContrastiveLoss with these parameters:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 1.0, 4 "size_average": true 5}

Evaluation Dataset

Unnamed Dataset

Size: 760 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 760 samples:
sentence1 sentence2 label
type string string int
details
min: 4 tokens
mean: 33.98 tokens
max: 230 tokens
min: 4 tokens
mean: 31.82 tokens
max: 192 tokens
0: ~58.55%
1: ~41.45%

Samples:

sentence1	sentence2	label
`I'm not getting enough nutrients ?[object Object][object Object]I have a lot of unhealthy coping mechanisms and I'm trying to not SH as much anymore, which makes that the ED is coming up much stronger? [object Object]I don't know what to do or how to stop this, anyone have any advice?[object Object][object Object]Edit: I also have to say that when I eat I get really nauseous, but when I don't eat I also get nauseous, which then makes it harder to eat and it just keeps going on like that`	`I want to be smaller than them and all that because eds are like that, but that's hard when they don't tell me stuff and I'm left to stew in my own thoughts`	`0`
`I'm underweight right now and I was actually gaining weight since I was an average weight about two-three weeks ago and went down`	`So, this is definetly going into the right direction, but:[object Object] I am so afraid of stepping on the scale and seeing gained weight`	`1`
`I feel so much shame around the idea of eating junk food and often times, when things have gotten worse, part of that mechanism has been my definition of "junk food" getting so warped that I consider almost anything unhealthy and shameful to eat`	`Overall, I realized my eating disorders had almost nothing to do with food`	`0`

Loss: ContrastiveLoss with these parameters:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 1.0, 4 "size_average": true 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 2
learning_rate: 1.5e-05
num_train_epochs: 5
warmup_ratio: 0.1
bf16: True
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: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1.5e-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: 5
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: True
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: 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: 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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	quora_duplicates_dev_cosine_ap
0.5333	100	0.2145	-	-
1.064	200	0.185	0.0903	-
1.5973	300	0.1472	-	-
2.128	400	0.1361	0.0914	-
2.6613	500	0.1045	-	-
3.192	600	0.0879	0.0957	-
3.7253	700	0.067	-	-
4.256	800	0.0617	0.1003	-
4.7893	900	0.0529	-	-
-1	-1	-	-	0.7166

Framework Versions

Python: 3.10.8
Sentence Transformers: 5.0.0
Transformers: 4.49.0
PyTorch: 2.1.2+cu118
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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",
}

ContrastiveLoss

bibtex

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

	sentence1	sentence2	label
type	string	string	int
details	min: 4 tokens mean: 33.52 tokens max: 169 tokens	min: 5 tokens mean: 32.67 tokens max: 228 tokens	0: ~57.10% 1: ~42.90%

IrisCHEN04/Sentence-Transformer_Roberta_Reddit_ed

SentenceTransformer

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Binary Classification

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

ContrastiveLoss

IrisCHEN04/
Sentence-Transformer_Roberta_Reddit_ed