Modelpublic

albertus-sussex/veriscrape-sbert-movie-wo-ref-gemini-1.5-flash

sourceHugging Faceupdated 1y agoView on Hugging Face

0likes18downloads

Model Card

SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-base-en-v1.5. 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: Alibaba-NLP/gte-base-en-v1.5
—Maximum Sequence Length: 32 tokens
—Output Dimensionality: 768 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': 32, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("albertus-sussex/veriscrape-sbert-movie-wo-ref-gemini-1.5-flash")
# Run inference
sentences = [
    '»',
    '»',
    'Cats & Dogs',
]
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]

<!--

Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details> -->

<!--

Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details> -->

<!--

Out-of-Scope Use

List how the model may foreseeably be misused and address what users ought not to do with the model. -->

Evaluation

Metrics

Triplet

—Evaluated with <code>TripletEvaluator</code>

Metric	Value
cosine_accuracy	0.9995

Silhouette

—Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

Metric	Value
silhouette_cosine	0.7177
silhouette_euclidean	0.5843

Triplet

—Evaluated with <code>TripletEvaluator</code>

Metric	Value
cosine_accuracy	0.9994

Silhouette

—Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

Metric	Value
silhouette_cosine	0.7135
silhouette_euclidean	0.5822

<!--

Bias, Risks and Limitations

What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -->

<!--

Recommendations

What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -->

Training Details

Training Dataset

Unnamed Dataset

—Size: 38,612 training samples
—Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>posattrname</code>, <code>negattrname</code>, and <code>website_id</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | posattrname | negattrname | website_id | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 6.03 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.98 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.14 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.68 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.42 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>0: ~1.70%</li><li>1: ~4.00%</li><li>2: ~3.80%</li><li>3: ~26.80%</li><li>4: ~2.70%</li><li>5: ~25.30%</li><li>6: ~23.20%</li><li>7: ~3.90%</li><li>8: ~4.90%</li><li>9: ~3.70%</li></ul> |
—Samples: | anchor | positive | negative | posattrname | negattrname | websiteid | |:----------------------------------------------------|:----------------------------------------|:-----------------------------|:-------------------|:-------------------------|:---------------| | <code>Horror</code> | <code>Action</code> | <code>Martin Scorsese</code> | <code>genre</code> | <code>director</code> | <code>4</code> | | <code>Megamind</code> | <code>It's Kind of a Funny Story</code> | <code>TV-G</code> | <code>title</code> | <code>mpaarating</code> | <code>6</code> | | <code>Something Wicked This Way Comes (1983)</code> | <code>Kandahar (2001)</code> | <code>(PG)</code> | <code>title</code> | <code>mpaa_rating</code> | <code>3</code> |
—Loss: <code>TripletLoss</code> with these parameters:

json

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }

Evaluation Dataset

Unnamed Dataset

—Size: 4,291 evaluation samples
—Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>posattrname</code>, <code>negattrname</code>, and <code>website_id</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | posattrname | negattrname | website_id | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 5.64 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.79 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.23 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.54 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.48 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>0: ~2.20%</li><li>1: ~4.90%</li><li>2: ~5.40%</li><li>3: ~27.00%</li><li>4: ~2.40%</li><li>5: ~19.80%</li><li>6: ~22.80%</li><li>7: ~4.30%</li><li>8: ~7.30%</li><li>9: ~3.90%</li></ul> |
—Samples: | anchor | positive | negative | posattrname | negattrname | websiteid | |:-----------------------------------|:--------------------------|:-------------------------|:-------------------|:-------------------------|:---------------| | <code>My Fake Fiance (2009)</code> | <code>Dames (1934)</code> | <code>Comedy</code> | <code>title</code> | <code>genre</code> | <code>4</code> | | <code>Music Drama</code> | <code>Unknown</code> | <code>PG-13</code> | <code>genre</code> | <code>mpaarating</code> | <code>2</code> | | <code>Due Date</code> | <code>Megamind</code> | <code>Ben Younger</code> | <code>title</code> | <code>director</code> | <code>6</code> |
—Loss: <code>TripletLoss</code> with these parameters:

json

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: epoch
—per_device_train_batch_size: 128
—per_device_eval_batch_size: 128
—num_train_epochs: 5
—warmup_ratio: 0.1

All Hyperparameters

<details><summary>Click to expand</summary>

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: epoch
—prediction_loss_only: True
—per_device_train_batch_size: 128
—per_device_eval_batch_size: 128
—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: 1.0
—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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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
—use_liger_kernel: False
—eval_use_gather_object: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: proportional

</details>

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy	silhouette_cosine
-1	-1	-	-	0.5540	0.0936
1.0	302	0.4164	0.0137	0.9991	0.6674
2.0	604	0.0161	0.0107	0.9991	0.7327
3.0	906	0.0063	0.0070	0.9995	0.7161
4.0	1208	0.0036	0.0057	0.9995	0.7182
5.0	1510	0.002	0.0064	0.9995	0.7177
-1	-1	-	-	0.9994	0.7135

Framework Versions

—Python: 3.10.16
—Sentence Transformers: 4.0.1
—Transformers: 4.45.2
—PyTorch: 2.5.1+cu124
—Accelerate: 1.6.0
—Datasets: 3.1.0
—Tokenizers: 0.20.3

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}
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->