---
base_model: manuel-couto-pintos/roberta_erisk
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:50881
- loss:TripletLoss
widget:
- source_sentence: I smoked weed for the first time ever a couple days ago, how long
until it's out of my system?
sentences:
- If I haven't smoked weed in a long time and smoked 1 day, how long will it be
in my urine?
- Where can we find best delay pedal?
- How long does it take for an avid weed smoker to pass a urine drug test?
- source_sentence: What are the visiting places in coorg?
sentences:
- How can I find a co-working space in Gurgaon?
- What are the places to visit in coorg?
- What are your favourite celebrity cookbooks?
- source_sentence: What is the best used car to get under 5k?
sentences:
- What's the best used car for under 5k?
- What do you think about RBI's new move of banning 500 and 1000 notes?
- Which is the best car to buy under 6 lakhs?
- source_sentence: Which exercises can I do at home to reduce belly fat?
sentences:
- What exercise we can do to reduce belly fat at home?
- What is a first time home buyer?
- My upper body is in shape but my thighs are very fatty and big ...so how can I
reduce my thighs .I am doing running of 3km daily only?
- source_sentence: Which is the best affiliate program?
sentences:
- How can I learn to make good coffee at home?
- What are the best affiliate networks in the UK?
- What are the best affiliate programs?
---
# SentenceTransformer based on manuel-couto-pintos/roberta_erisk
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [manuel-couto-pintos/roberta_erisk](https://huggingface.co/manuel-couto-pintos/roberta_erisk). 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:** [manuel-couto-pintos/roberta_erisk](https://huggingface.co/manuel-couto-pintos/roberta_erisk)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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("manuel-couto-pintos/roberta_erisk_sts")
# Run inference
sentences = [
'Which is the best affiliate program?',
'What are the best affiliate programs?',
'What are the best affiliate networks in the UK?',
]
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: 50,881 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: 6 tokens
- mean: 13.77 tokens
- max: 42 tokens
| - min: 6 tokens
- mean: 13.82 tokens
- max: 57 tokens
| - min: 6 tokens
- mean: 14.96 tokens
- max: 59 tokens
|
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:---------------------------------------------------------------|:--------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|
| What is a good definition of Quora? | What is the best definition of Quora? | What is Quora address? |
| How can I make myself appear offline on facebook? | How do you make sure to appear as offline on Facebook? | How can I get Facebook to remember to keep chat offline? |
| How do I gain some healthy weight? | What is the best way for underweight to gain weight? | My boyfriend doesn't eat a lot. What are some ways to help him gain weight fast? He's 5'7 120lbs |
* Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `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`: 10
- `per_device_eval_batch_size`: 10
- `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
Click to expand
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0983 | 500 | 4.3807 |
| 0.1965 | 1000 | 2.5872 |
| 0.2948 | 1500 | 1.7484 |
| 0.3930 | 2000 | 1.2649 |
| 0.4913 | 2500 | 1.0219 |
| 0.5895 | 3000 | 0.8703 |
| 0.6878 | 3500 | 0.771 |
| 0.7860 | 4000 | 0.655 |
| 0.8843 | 4500 | 0.6547 |
| 0.9825 | 5000 | 0.5772 |
| 1.0808 | 5500 | 0.5628 |
| 1.1790 | 6000 | 0.5163 |
| 1.2773 | 6500 | 0.4871 |
| 1.3755 | 7000 | 0.4842 |
| 1.4738 | 7500 | 0.4316 |
| 1.5720 | 8000 | 0.4199 |
| 1.6703 | 8500 | 0.3554 |
| 1.7685 | 9000 | 0.3467 |
| 1.8668 | 9500 | 0.3591 |
| 1.9650 | 10000 | 0.3356 |
| 2.0633 | 10500 | 0.3281 |
| 2.1615 | 11000 | 0.3149 |
| 2.2598 | 11500 | 0.2767 |
| 2.3580 | 12000 | 0.2849 |
| 2.4563 | 12500 | 0.244 |
| 2.5545 | 13000 | 0.2416 |
| 2.6528 | 13500 | 0.2008 |
| 2.7510 | 14000 | 0.1718 |
| 2.8493 | 14500 | 0.188 |
| 2.9475 | 15000 | 0.1656 |
| 3.0458 | 15500 | 0.1522 |
| 3.1440 | 16000 | 0.144 |
| 3.2423 | 16500 | 0.1329 |
| 3.3405 | 17000 | 0.1431 |
| 3.4388 | 17500 | 0.128 |
| 3.5370 | 18000 | 0.1251 |
| 3.6353 | 18500 | 0.0921 |
| 3.7335 | 19000 | 0.0882 |
| 3.8318 | 19500 | 0.1087 |
| 3.9300 | 20000 | 0.0819 |
| 4.0283 | 20500 | 0.0916 |
| 4.1265 | 21000 | 0.0837 |
| 4.2248 | 21500 | 0.0855 |
| 4.3230 | 22000 | 0.0727 |
| 4.4213 | 22500 | 0.0772 |
| 4.5196 | 23000 | 0.0676 |
| 4.6178 | 23500 | 0.0597 |
| 4.7161 | 24000 | 0.0555 |
| 4.8143 | 24500 | 0.0613 |
| 4.9126 | 25000 | 0.0589 |
| 5.0108 | 25500 | 0.0503 |
| 5.1091 | 26000 | 0.0546 |
| 5.2073 | 26500 | 0.0446 |
| 5.3056 | 27000 | 0.0591 |
| 5.4038 | 27500 | 0.0431 |
| 5.5021 | 28000 | 0.0402 |
| 5.6003 | 28500 | 0.0354 |
| 5.6986 | 29000 | 0.0405 |
| 5.7968 | 29500 | 0.0308 |
| 5.8951 | 30000 | 0.0363 |
| 5.9933 | 30500 | 0.0365 |
| 6.0916 | 31000 | 0.0333 |
| 6.1898 | 31500 | 0.0238 |
| 6.2881 | 32000 | 0.0372 |
| 6.3863 | 32500 | 0.0331 |
| 6.4846 | 33000 | 0.0253 |
| 6.5828 | 33500 | 0.0315 |
| 6.6811 | 34000 | 0.0193 |
| 6.7793 | 34500 | 0.0239 |
| 6.8776 | 35000 | 0.0201 |
| 6.9758 | 35500 | 0.0213 |
| 7.0741 | 36000 | 0.0187 |
| 7.1723 | 36500 | 0.0125 |
| 7.2706 | 37000 | 0.0151 |
| 7.3688 | 37500 | 0.0208 |
| 7.4671 | 38000 | 0.0101 |
| 7.5653 | 38500 | 0.0191 |
| 7.6636 | 39000 | 0.0125 |
| 7.7618 | 39500 | 0.0136 |
| 7.8601 | 40000 | 0.0135 |
| 7.9583 | 40500 | 0.0118 |
| 8.0566 | 41000 | 0.012 |
| 8.1548 | 41500 | 0.0079 |
| 8.2531 | 42000 | 0.0105 |
| 8.3513 | 42500 | 0.0094 |
| 8.4496 | 43000 | 0.0079 |
| 8.5478 | 43500 | 0.0118 |
| 8.6461 | 44000 | 0.0105 |
| 8.7444 | 44500 | 0.0058 |
| 8.8426 | 45000 | 0.013 |
| 8.9409 | 45500 | 0.0065 |
| 9.0391 | 46000 | 0.0089 |
| 9.1374 | 46500 | 0.0031 |
| 9.2356 | 47000 | 0.008 |
| 9.3339 | 47500 | 0.0065 |
| 9.4321 | 48000 | 0.0052 |
| 9.5304 | 48500 | 0.0066 |
| 9.6286 | 49000 | 0.0039 |
| 9.7269 | 49500 | 0.004 |
| 9.8251 | 50000 | 0.0051 |
| 9.9234 | 50500 | 0.003 |