This is a sentence-transformers model finetuned from distilbert/distilroberta-base. It maps sentences & paragraphs to a 768-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: distilbert/distilroberta-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("iqrakiran/my-sentence-transformer-model")
# Run inference
sentences = [
    "I remember generating alternative predictions. We said that I might get nervous but if I stay focused on what I want to do, I can probably do it. It's just hard to believe when I'm in this situation.",
    "It's normal to feel nervous in difficult situations. As a counsellor, I suggest continuing to test these alternative predictions directly in real-life experiences. As you encounter situations where you manage to handle things even while nervous, your belief in your abilities and the predictions will strengthen. Remember to stay focused and trust yourself.",
    'If you are a counsellor, please answer the questions based on the description of the patient.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 4,093 training samples
Columns: sentence_0, sentence_1, and sentence_2
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2
type string string string
details
min: 11 tokens
mean: 57.82 tokens
max: 197 tokens
min: 30 tokens
mean: 76.95 tokens
max: 149 tokens
min: 22 tokens
mean: 22.0 tokens
max: 22 tokens

Samples:

sentence_0	sentence_1	sentence_2
`Lately, I've been feeling really stressed out and I keep having this thought that my partner might be cheating on me. It started when I noticed changes in their behavior a couple of months ago. At times, I feel like my partner acts distant, but when I confront them about it, they assure me that everything is fine between us. I feel like maybe it's not a strong enough thought to be classified as a delusion, but it's really bothering me and affecting our relationship.`	Thank you for sharing your concerns and feelings with me. It's important to know that it's normal for people to experience doubts and insecurities in relationships sometimes. I understand that feeling stressed and having negative thoughts is difficult to deal with. If you're open to it, perhaps we could explore these thoughts and feelings further to better understand their origin and find ways to cope with them. Additionally, involving your partner in a constructive conversation about your feelings and concerns might help to improve your relationship and provide reassurance.	`If you are a counsellor, please answer the questions based on the description of the patient.`
`I think counting would mean that somebody cares about me and I matter to them. In my life, for example, I think Ms. Jones is upset that I am not as involved or doing the things that she saw me do last year. So I guess I matter to her.`	`It's important to recognize that feeling valued and cared for by others can contribute to our overall well-being. It's great that you have someone like Ms. Jones in your life who notices your changes and seems to care about you. Can you tell me more about your relationship with Ms. Jones or the changes that have occurred in the past year?`	`If you are a counsellor, please answer the questions based on the description of the patient.`
`Lately, I've been feeling quite down, and it's been affecting my daily life. I've also been having a hard time concentrating and making decisions, and often find myself lost in thought. On top of that, I've started hearing voices that aren't there, but it only happens occasionally. It all started when I moved to a new city a few months ago. I feel so isolated, and the stress of adjusting to a new environment might be causing these issues.`	Thank you for sharing your feelings and experiences with me. It sounds like you are struggling with some challenging emotions and experiences since your move to a new city. Adjusting to a new environment can be a significant source of stress. It's essential to acknowledge what you're going through and understand that seeking help is healthy. Can you provide further details about the voices you've been hearing? How often do they occur, and how exactly do they affect your daily life? Also, can you share your previous mental health history, if any? Your additional information will help me get a better understanding of your situation and provide the best support possible.	`If you are a counsellor, please answer the questions based on the description of the patient.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 10
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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: 1
eval_accumulation_steps: None
torch_empty_cache_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
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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: 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
eval_on_start: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
1.9531	500	0.2321
3.9062	1000	0.0
5.8594	1500	0.0001
7.8125	2000	0.0
9.7656	2500	0.0

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 11 tokens mean: 57.82 tokens max: 197 tokens	min: 30 tokens mean: 76.95 tokens max: 149 tokens	min: 22 tokens mean: 22.0 tokens max: 22 tokens