checkpoint-55/README.md

metadata

base_model: microsoft/deberta-v3-small
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:116445
  - loss:CachedGISTEmbedLoss
widget:
  - source_sentence: what is the main purpose of the brain
    sentences:
      - >-
        Brain Physiologically, the function of the brain is to exert centralized
        control over the other organs of the body. The brain acts on the rest of
        the body both by generating patterns of muscle activity and by driving
        the secretion of chemicals called hormones. This centralized control
        allows rapid and coordinated responses to changes in the environment.
        Some basic types of responsiveness such as reflexes can be mediated by
        the spinal cord or peripheral ganglia, but sophisticated purposeful
        control of behavior based on complex sensory input requires the
        information integrating capabilities of a centralized brain.
      - >-
        How do scientists know that some mountains were once at the bottom of an
        ocean?
      - >-
        The Smiths Wiki | Fandom powered by Wikia Share Ad blocker interference
        detected! Wikia is a free-to-use site that makes money from advertising.
        We have a modified experience for viewers using ad blockers Wikia is not
        accessible if you’ve made further modifications. Remove the custom ad
        blocker rule(s) and the page will load as expected. The Smiths were an
        English rock band formed in Manchester in 1982. Based on the songwriting
        partnership of Morrissey (vocals) and Johnny Marr (guitar), the band
        also included Andy Rourke (bass), Mike Joyce (drums) and for a brief
        time Craig Gannon (rhythm guitar). Critics have called them one of the
        most important alternative rock bands to emerge from the British
        independent music scene of the 1980s,and the group has had major
        influence on subsequent artists. Morrissey's lovelorn tales of
        alienation found an audience amongst youth culture bored by the
        ubiquitous synthesiser-pop bands of the early 1980s, while Marr's
        complex melodies helped return guitar-based music to popularity. The
        group were signed to the independent record label Rough Trade Records ,
        for whom they released four studio albums and several compilations, as
        well as numerous non-LP singles. Although they had limited commercial
        success outside the UK while they were still together, and never
        released a single that charted higher than number 10 in their home
        country, The Smiths won a growing following, and they remain cult and
        commercial favourites. The band broke up in 1987 amid disagreements
        between Morrissey and Marr and has turned down several offers to reform.
        Welcome to The Smiths Wiki
  - source_sentence: There were 29 Muslims fatalities in the Cave of the Patriarchs massacre .
    sentences:
      - >-
        In August , after the end of the war in June 1902 , Higgins Southampton
        left the `` SSBavarian '' and returned to Cape Town the following month
        .
      - >-
        Between 29 and 52 Muslims were killed and more than 100 others wounded .
        [   Settlers remember gunman Goldstein ; Hebron riots continue ] .
      - >-
        29 Muslims were killed and more than 100 others wounded . [   Settlers
        remember gunman Goldstein ; Hebron riots continue ] .
  - source_sentence: are tabby cats all male?
    sentences:
      - >-
        Did you know orange tabby cats are typically male? In fact, up to 80
        percent of orange tabbies are male, making orange female cats a bit of a
        rarity. According to the BBC's Focus Magazine, the ginger gene in cats
        works a little differently compared to humans; it is on the X
        chromosome.
      - >-
        Shawnee Trails Council was formed from the merger of the Four Rivers
        Council and the Audubon Council .
      - |
        A picture of a modern looking kitchen area
  - source_sentence: >-
      Aamir Khan agreed to act immediately after reading Mehra 's screenplay in
      `` Rang De Basanti '' .
    sentences:
      - >-
        Chris Rea —   Free listening, videos, concerts, stats and photos at
        Last.fm singer-songwriter Christopher Anton Rea (pronounced Ree-ah),
        born 4 March 1951, is a singer, songwriter, and guitarist from
        Middlesbrough, England. Rea's recording career began in 1978. Although
        he almost immediately had a US hit single with "Fool (If You Think It's
        Over)", Rea's initial focus was on continental Europe, releasing eight
        albums in the 1980s. It wasn't until 1985's Shamrock Diaries and the
        songs "Stainsby Girls" and "Josephine," that UK audiences began to take
        notice of him. Follow up albums… read more
      - "Healthy Fast Food Meal No. 1. Grilled Chicken Sandwich and Fruit Cup (Chick-fil-A) Several fast food chains offer a grilled chicken sandwich. The trick is ordering it without mayo or creamy sauce, and making sure itâ\x80\x99s served with a whole grain bun."
      - >-
        Aamir Khan agreed to act in `` Rang De Basanti '' immediately after
        reading Mehra 's script .
  - source_sentence: 'A man wearing a blue bow tie and a fedora hat in a car. '
    sentences:
      - A man takes a photo of himself wearing a bowtie and hat
      - Scientists explain the world based on what?
      - "County of Angus - definition of County of Angus by The Free Dictionary County of Angus - definition of County of Angus by The Free Dictionary http://www.thefreedictionary.com/County+of+Angus \_(ăng′gəs) n. Any of a breed of hornless beef cattle that originated in Scotland and are usually black but also occur in a red variety. Also called Black Angus. [After Angus, former county of Scotland.] Angus (ˈæŋɡəs) n (Placename) a council area of E Scotland on the North Sea: the historical county of Angus became part of Tayside region in 1975; reinstated as a unitary authority (excluding City of Dundee) in 1996. Administrative centre: Forfar. Pop: 107 520 (2003 est). Area: 2181 sq km (842 sq miles) An•gus"
model-index:
  - name: SentenceTransformer based on microsoft/deberta-v3-small
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.2589065791031549
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.31323211323674593
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.27236487282828553
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.29656486394161036
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.2585939429800171
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.2833925986586202
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.28511212645281553
            name: Pearson Dot
          - type: spearman_dot
            value: 0.2967423026930272
            name: Spearman Dot
          - type: pearson_max
            value: 0.28511212645281553
            name: Pearson Max
          - type: spearman_max
            value: 0.31323211323674593
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allNLI dev
          type: allNLI-dev
        metrics:
          - type: cosine_accuracy
            value: 0.66796875
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.9721465110778809
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.5343511450381679
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.85741126537323
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.39886039886039887
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8092485549132948
            name: Cosine Recall
          - type: cosine_ap
            value: 0.4140638596370657
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.666015625
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 518.88671875
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.514018691588785
            name: Dot F1
          - type: dot_f1_threshold
            value: 323.9651184082031
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.35181236673773986
            name: Dot Precision
          - type: dot_recall
            value: 0.953757225433526
            name: Dot Recall
          - type: dot_ap
            value: 0.3781233337023534
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.671875
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 114.41839599609375
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.5384615384615384
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 226.82566833496094
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.3941018766756032
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8497109826589595
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.4272864144491257
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.671875
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 5.084325790405273
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.5404339250493098
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 11.333902359008789
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.4101796407185629
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.791907514450867
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.41769294415599645
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.671875
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 518.88671875
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.5404339250493098
            name: Max F1
          - type: max_f1_threshold
            value: 323.9651184082031
            name: Max F1 Threshold
          - type: max_precision
            value: 0.4101796407185629
            name: Max Precision
          - type: max_recall
            value: 0.953757225433526
            name: Max Recall
          - type: max_ap
            value: 0.4272864144491257
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Qnli dev
          type: Qnli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.640625
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8695281744003296
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.6578512396694215
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7936367988586426
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.5392953929539296
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8432203389830508
            name: Cosine Recall
          - type: cosine_ap
            value: 0.6314640856589909
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.609375
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 351.17626953125
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.6501650165016502
            name: Dot F1
          - type: dot_f1_threshold
            value: 316.48046875
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5324324324324324
            name: Dot Precision
          - type: dot_recall
            value: 0.8347457627118644
            name: Dot Recall
          - type: dot_ap
            value: 0.5366456296706419
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.658203125
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 206.32894897460938
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.652373660030628
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 261.3590393066406
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5107913669064749
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.902542372881356
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.6679289689394285
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.65234375
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 10.764808654785156
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.6393210749646393
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 15.096710205078125
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.47983014861995754
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9576271186440678
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.6460602994393339
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.658203125
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 351.17626953125
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.6578512396694215
            name: Max F1
          - type: max_f1_threshold
            value: 316.48046875
            name: Max F1 Threshold
          - type: max_precision
            value: 0.5392953929539296
            name: Max Precision
          - type: max_recall
            value: 0.9576271186440678
            name: Max Recall
          - type: max_ap
            value: 0.6679289689394285
            name: Max Ap

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from microsoft/deberta-v3-small on the bobox/enhanced_nli-50_k dataset. 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • bobox/enhanced_nli-50_k

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): 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTa-small-ST-UnifiedDatasets-baseline-checkpoints-tmp")
# Run inference
sentences = [
    'A man wearing a blue bow tie and a fedora hat in a car. ',
    'A man takes a photo of himself wearing a bowtie and hat',
    'County of Angus - definition of County of Angus by The Free Dictionary County of Angus - definition of County of Angus by The Free Dictionary http://www.thefreedictionary.com/County+of+Angus \xa0(ăng′gəs) n. Any of a breed of hornless beef cattle that originated in Scotland and are usually black but also occur in a red variety. Also called Black Angus. [After Angus, former county of Scotland.] Angus (ˈæŋɡəs) n (Placename) a council area of E Scotland on the North Sea: the historical county of Angus became part of Tayside region in 1975; reinstated as a unitary authority (excluding City of Dundee) in 1996. Administrative centre: Forfar. Pop: 107 520 (2003 est). Area: 2181 sq km (842 sq miles) An•gus',
]
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]

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.2589
spearman_cosine	0.3132
pearson_manhattan	0.2724
spearman_manhattan	0.2966
pearson_euclidean	0.2586
spearman_euclidean	0.2834
pearson_dot	0.2851
spearman_dot	0.2967
pearson_max	0.2851
spearman_max	0.3132

Binary Classification

• Dataset: allNLI-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.668
cosine_accuracy_threshold	0.9721
cosine_f1	0.5344
cosine_f1_threshold	0.8574
cosine_precision	0.3989
cosine_recall	0.8092
cosine_ap	0.4141
dot_accuracy	0.666
dot_accuracy_threshold	518.8867
dot_f1	0.514
dot_f1_threshold	323.9651
dot_precision	0.3518
dot_recall	0.9538
dot_ap	0.3781
manhattan_accuracy	0.6719
manhattan_accuracy_threshold	114.4184
manhattan_f1	0.5385
manhattan_f1_threshold	226.8257
manhattan_precision	0.3941
manhattan_recall	0.8497
manhattan_ap	0.4273
euclidean_accuracy	0.6719
euclidean_accuracy_threshold	5.0843
euclidean_f1	0.5404
euclidean_f1_threshold	11.3339
euclidean_precision	0.4102
euclidean_recall	0.7919
euclidean_ap	0.4177
max_accuracy	0.6719
max_accuracy_threshold	518.8867
max_f1	0.5404
max_f1_threshold	323.9651
max_precision	0.4102
max_recall	0.9538
max_ap	0.4273

Binary Classification

• Dataset: Qnli-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6406
cosine_accuracy_threshold	0.8695
cosine_f1	0.6579
cosine_f1_threshold	0.7936
cosine_precision	0.5393
cosine_recall	0.8432
cosine_ap	0.6315
dot_accuracy	0.6094
dot_accuracy_threshold	351.1763
dot_f1	0.6502
dot_f1_threshold	316.4805
dot_precision	0.5324
dot_recall	0.8347
dot_ap	0.5366
manhattan_accuracy	0.6582
manhattan_accuracy_threshold	206.3289
manhattan_f1	0.6524
manhattan_f1_threshold	261.359
manhattan_precision	0.5108
manhattan_recall	0.9025
manhattan_ap	0.6679
euclidean_accuracy	0.6523
euclidean_accuracy_threshold	10.7648
euclidean_f1	0.6393
euclidean_f1_threshold	15.0967
euclidean_precision	0.4798
euclidean_recall	0.9576
euclidean_ap	0.6461
max_accuracy	0.6582
max_accuracy_threshold	351.1763
max_f1	0.6579
max_f1_threshold	316.4805
max_precision	0.5393
max_recall	0.9576
max_ap	0.6679

Training Details

Training Dataset

bobox/enhanced_nli-50_k

• Dataset: bobox/enhanced_nli-50_k
• Size: 116,445 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 4 tokens mean: 33.67 tokens max: 338 tokens	min: 2 tokens mean: 51.48 tokens max: 512 tokens

• Samples:

  sentence1	sentence2
  who is darnell from my name is earl	Eddie Steeples Eddie Steeples (born November 25, 1973)[1] is an American actor known for his roles as the "Rubberband Man" in an advertising campaign for OfficeMax, and as Darnell Turner on the NBC sitcom My Name Is Earl.
  Ferrell and the Chili Peppers toured together in 2013 .	Ferrell and the Chili Peppers wrapped up I 'm With You World Tour in April 2013 .
  Cells have four cycles.	How many cycles do cells have?

• Loss: CachedGISTEmbedLoss 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

bobox/enhanced_nli-50_k

• Dataset: bobox/enhanced_nli-50_k
• Size: 1,506 evaluation samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 3 tokens mean: 32.36 tokens max: 341 tokens	min: 2 tokens mean: 61.99 tokens max: 431 tokens

• Samples:

  sentence1	sentence2
  Interestingly, snakes use their forked tongues to smell.	Snakes use their tongue to smell things.
  Soil is a renewable resource that can take thousand of years to form.	What is a renewable resource that can take thousand of years to form?
  As of March 22 , there were more than 321,000 cases with over 13,600 deaths and more than 96,000 recoveries reported worldwide .	As of 22 March , more than 321,000 cases of COVID-19 have been reported in over 180 countries and territories , resulting in more than 13,600 deaths and 96,000 recoveries .

• Loss: CachedGISTEmbedLoss 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: 640
• per_device_eval_batch_size: 128
• learning_rate: 3.75e-05
• weight_decay: 0.0005
• lr_scheduler_type: cosine_with_min_lr
• lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 7.499999999999999e-06}
• warmup_ratio: 0.33
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: bobox/DeBERTa-small-ST-UnifiedDatasets-baseline-checkpoints-tmp
• hub_strategy: all_checkpoints
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 640
• 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: 3.75e-05
• weight_decay: 0.0005
• 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: cosine_with_min_lr
• lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 7.499999999999999e-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: {'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: True
• resume_from_checkpoint: None
• hub_model_id: bobox/DeBERTa-small-ST-UnifiedDatasets-baseline-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

Training Logs

Epoch	Step	Training Loss	loss	Qnli-dev_max_ap	allNLI-dev_max_ap	sts-test_spearman_cosine
0.0055	1	8.8159	-	-	-	-
0.0110	2	9.1259	-	-	-	-
0.0165	3	8.9017	-	-	-	-
0.0220	4	9.1969	-	-	-	-
0.0275	5	9.3716	1.3746	0.6067	0.3706	0.1943
0.0330	6	9.0425	-	-	-	-
0.0385	7	8.7309	-	-	-	-
0.0440	8	9.0123	-	-	-	-
0.0495	9	8.8095	-	-	-	-
0.0549	10	9.3194	1.3227	0.6089	0.3721	0.1976
0.0604	11	8.9873	-	-	-	-
0.0659	12	8.5575	-	-	-	-
0.0714	13	8.8096	-	-	-	-
0.0769	14	8.0996	-	-	-	-
0.0824	15	8.1942	1.2244	0.6140	0.3743	0.2085
0.0879	16	8.1654	-	-	-	-
0.0934	17	7.7336	-	-	-	-
0.0989	18	7.9535	-	-	-	-
0.1044	19	7.9322	-	-	-	-
0.1099	20	7.6812	1.1301	0.6199	0.3790	0.2233
0.1154	21	7.551	-	-	-	-
0.1209	22	7.3788	-	-	-	-
0.1264	23	7.1746	-	-	-	-
0.1319	24	7.1849	-	-	-	-
0.1374	25	7.1085	1.0723	0.6195	0.3852	0.2357
0.1429	26	7.3926	-	-	-	-
0.1484	27	7.1817	-	-	-	-
0.1538	28	7.239	-	-	-	-
0.1593	29	7.0023	-	-	-	-
0.1648	30	6.9898	1.0282	0.6215	0.3898	0.2477
0.1703	31	6.9776	-	-	-	-
0.1758	32	6.8088	-	-	-	-
0.1813	33	6.8916	-	-	-	-
0.1868	34	6.6931	-	-	-	-
0.1923	35	6.5707	0.9846	0.6253	0.3952	0.2608
0.1978	36	6.6231	-	-	-	-
0.2033	37	6.4951	-	-	-	-
0.2088	38	6.4607	-	-	-	-
0.2143	39	6.4504	-	-	-	-
0.2198	40	6.3649	0.9314	0.6299	0.4041	0.2738
0.2253	41	6.2244	-	-	-	-
0.2308	42	6.007	-	-	-	-
0.2363	43	5.977	-	-	-	-
0.2418	44	6.0748	-	-	-	-
0.2473	45	5.7946	0.8549	0.6404	0.4116	0.2847
0.2527	46	5.8751	-	-	-	-
0.2582	47	5.543	-	-	-	-
0.2637	48	5.5511	-	-	-	-
0.2692	49	5.411	-	-	-	-
0.2747	50	5.378	0.7943	0.6557	0.4159	0.2866
0.2802	51	5.3831	-	-	-	-
0.2857	52	4.9729	-	-	-	-
0.2912	53	5.0425	-	-	-	-
0.2967	54	4.9446	-	-	-	-
0.3022	55	4.9288	0.7178	0.6679	0.4273	0.3132

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.0.1
• Transformers: 4.44.0
• PyTorch: 2.4.0
• Accelerate: 0.33.0
• Datasets: 2.21.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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",
}