---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2270482
- loss:BatchHardSoftMarginTripletLoss
base_model: dunzhang/stella_en_400M_v5
widget:
- source_sentence: 'infocus - projector lamp - 2500 hour ( s infocus splamp 043 replacement
lamp infocus certified lamp for the in11 ... '
sentences:
- ' be careful , your gonna have to smash up gim after all that'
- 'mann i aint talk to my sis n like 4ever hun call me imy '
- darling , i will do u one beta and personally bring sum " ship me sum lol rt i
made some shrimp tonight .. curry
- source_sentence: 'how''d you do that ? ? rt my home screen '
sentences:
- ' indeed it is , but with my screen the way it is i won''t be able to check in
soon ! i am going in on thursday'
- ' awh are you at the hospital ? xxx'
- 'oppa , sorry i can''t watch ss4 and i can''t meet you '
- source_sentence: that sad moment when you want ice cream but you have no money
sentences:
- '" no josh stay in our asb , i have cookies "'
- ' was my phone off ? ?'
- ' no i don''t understand it !'
- source_sentence: ps ; dinner will be cooked shortly ! ) )
sentences:
- 'pachelbel : canon / other 17th - century music for 3 violins ( audio cd '
- ' hard to believe you got your panties all in a twist about something again hahaha
. sorry , i''ll shut up now .'
- im ready to let go , move on , be happy but there will always this little shred
of well maybe hell want me tomorrow .
- source_sentence: '#thesexwasntgoodif he can still move'
sentences:
- '" my righthand be bck soon " i came back lastnightt righthand'
- ' this has been going through my head all today babe ! missed seeing you sunday
xxx'
- ' hell yeaa it look good don''t worry .'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# stella-400M-twitter-cls-tuned
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5). 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
- **Base model:** [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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: NewModel
(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): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'#thesexwasntgoodif he can still move',
' this has been going through my head all today babe ! missed seeing you sunday xxx',
'" my righthand be bck soon " i came back lastnightt righthand',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,270,482 training samples
* Columns: sentence_0 and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------|
| type | string | int |
| details | - min: 4 tokens
- mean: 21.01 tokens
- max: 54 tokens
| |
* Samples:
| sentence_0 | label |
|:-----------------------------------------------------------------------------------------|:---------------|
| already miss the kids . such an exciting experience ! teacher for a day | 1 |
| have a beautiful day every 1 god bless you ! #ktbspa | 1 |
| i swear everytime i come to the barnyard something good happens #loveithere | 1 |
* Loss: [BatchHardSoftMarginTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchhardsoftmargintripletloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 250
- `per_device_eval_batch_size`: 250
- `num_train_epochs`: 2
- `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`: 250
- `per_device_eval_batch_size`: 250
- `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`: 2
- `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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin