bobox/DeBERTa3-s-CustomPoolin-toytest-step1

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from microsoft/deberta-v3-small. 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: microsoft/deberta-v3-small <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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: DebertaV2Model 
  (1): AdvancedWeightedPooling(
    (linear_cls_pj): Linear(in_features=768, out_features=768, bias=True)
    (linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True)
    (linear_mean_pj): Linear(in_features=768, out_features=768, bias=True)
    (linear_attnOut): Linear(in_features=768, out_features=768, bias=True)
    (mha): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
    )
    (layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layernorm_pjCls): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layernorm_pjMean): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-toytest-step1")
# Run inference
sentences = [
    'who was the first person who made the violin',
    'Violin The first makers of violins probably borrowed from various developments of the Byzantine lira. These included the rebec;[13] the Arabic rebab; the vielle (also known as the fidel or viuola); and the lira da braccio[11][14] The violin in its present form emerged in early 16th-century northern Italy. The earliest pictures of violins, albeit with three strings, are seen in northern Italy around 1530, at around the same time as the words "violino" and "vyollon" are seen in Italian and French documents. One of the earliest explicit descriptions of the instrument, including its tuning, is from the Epitome musical by Jambe de Fer, published in Lyon in 1556.[15] By this time, the violin had already begun to spread throughout Europe.',
    "Alice in Chains Alice in Chains is an American rock band from Seattle, Washington, formed in 1987 by guitarist and vocalist Jerry Cantrell and drummer Sean Kinney,[1] who recruited bassist Mike Starr[1] and lead vocalist Layne Staley.[1][2][3] Starr was replaced by Mike Inez in 1993.[4] After Staley's death in 2002, William DuVall joined in 2006 as co-lead vocalist and rhythm guitarist. The band took its name from Staley's previous group, the glam metal band Alice N' Chains.[5][2]",
]
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]

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Direct Usage (Transformers)

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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Out-of-Scope Use

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Evaluation

Metrics

Semantic Similarity

• —Dataset: sts-test
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Binary Classification

• —Dataset: allNLI-dev
• —Evaluated with <code>BinaryClassificationEvaluator</code>

Binary Classification

• —Dataset: Qnli-dev
• —Evaluated with <code>BinaryClassificationEvaluator</code>

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Recommendations

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 32,500 training samples
• —Columns: <code>sentence1</code> and <code>sentence2</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 29.3 tokens</li><li>max: 343 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 57.53 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>A Slippery Dick is what type of creature?</code> | <code>The Slippery Dick (Juvenile) - Whats That Fish! Description Also known as Sand-reef Wrasses and Slippery Dick Wrasse. Found singly or in pairs or in groups constantly circling around reefs, sea grass beds and sandy areas. Colours highly variable especially between juvenile to adult. They feed on hard shell invertebrates. Length - 18cm Depth - 2-12m Widespread Western Atlantic & Caribbean Most reef fish seen by divers during the day are grazers, that cruise around just above the surface of the coral or snoop into crevices looking for algae, worms and small crustaceans. Wrasses have small protruding teeth and graze the bottom taking in a variety of snails, worms, crabs, shrimps and eggs. Any hard coats or thick shells are then ground down by their pharyngeal jaws and the delicacies inside digested. From juvenile to adult wrasses dramatically alter their colour and body shapes. Wrasses are always on the go during the day, but are the first to go to bed and the last to rise. Small wrasses dive below the sand to sleep and larger wrasses wedge themselves in crevasses. Related creatures Heads up! Many creatures change during their life. Juvenile fish become adults and some change shape or their colour. Some species change sex and others just get older. The following creature(s) are known relatives of the Slippery Dick (Juvenile). Click the image(s) to explore further or hover over to get a better view! Slippery Dick</code> | | <code>e.&#9;in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently.</code> | <code>Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas</code> | | <code>In December 2015 , the film was ranked # 192 on IMDb .</code> | <code>As of December 2015 , it is the # 192 highest rated film on IMDb.</code> |
• —Loss: <code>GISTEmbedLoss</code> with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (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})
    (2): Normalize()
  ), 'temperature': 0.025}

Evaluation Dataset

Unnamed Dataset

• —Size: 1,664 evaluation samples
• —Columns: <code>sentence1</code> and <code>sentence2</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 28.74 tokens</li><li>max: 330 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 56.55 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What component of an organism, made up of many cells, in turn makes up an organ?</code> | <code></code> | | <code>Diffusion Diffusion is a process where atoms or molecules move from areas of high concentration to areas of low concentration.</code> | <code>Diffusion is the process in which a substance naturally moves from an area of higher to lower concentration.</code> | | <code>In the 1966 movie The Good, The Bad And The Ugly, Clint Eastwood played the Good" and Lee van Cleef played "the Bad", but who played "the Ugly"?</code> | <code>View All Photos (10) Movie Info In the last and the best installment of his so-called "Dollars" trilogy of Sergio Leone-directed "spaghetti westerns," Clint Eastwood reprised the role of a taciturn, enigmatic loner. Here he searches for a cache of stolen gold against rivals the Bad (Lee Van Cleef), a ruthless bounty hunter, and the Ugly (Eli Wallach), a Mexican bandit. Though dubbed "the Good," Eastwood's character is not much better than his opponents -- he is just smarter and shoots faster. The film's title reveals its ironic attitude toward the canonized heroes of the classical western. "The real West was the world of violence, fear, and brutal instincts," claimed Leone. "In pursuit of profit there is no such thing as good and evil, generosity or deviousness; everything depends on chance, and not the best wins but the luckiest." Immensely entertaining and beautifully shot in Techniscope by Tonino Delli Colli, the movie is a virtually definitive "spaghetti western," rivaled only by Leone's own Once Upon a Time in the West (1968). The main musical theme by Ennio Morricone hit #1 on the British pop charts. Originally released in Italy at 177 minutes, the movie was later cut for its international release. ~ Yuri German, Rovi Rating:</code> |
• —Loss: <code>GISTEmbedLoss</code> with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (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})
    (2): Normalize()
  ), 'temperature': 0.025}

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 256
• —lr_scheduler_type: cosinewithmin_lr
• —lr_scheduler_kwargs: {'numcycles': 0.5, 'minlr': 3.3333333333333337e-06}
• —warmup_ratio: 0.33
• —save_safetensors: False
• —fp16: True
• —push_to_hub: True
• —hub_model_id: bobox/DeBERTa3-s-CustomPoolin-toytest-step1-checkpoints-tmp
• —hub_strategy: all_checkpoints
• —batch_sampler: no_duplicates

All Hyperparameters

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

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: steps
• —prediction_loss_only: True
• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 256
• —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: 3
• —max_steps: -1
• —lr_scheduler_type: cosinewithmin_lr
• —lr_scheduler_kwargs: {'numcycles': 0.5, 'minlr': 3.3333333333333337e-06}
• —warmup_ratio: 0.33
• —warmup_steps: 0
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —save_safetensors: False
• —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: True
• —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: True
• —resume_from_checkpoint: None
• —hub_model_id: bobox/DeBERTa3-s-CustomPoolin-toytest-step1-checkpoints-tmp
• —hub_strategy: all_checkpoints
• —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: no_duplicates
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

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