---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:812
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: 1. What ergonomic factors contributed to the incidence of lateral
and medial epicondylitis among workers?
sentences:
- "ble, science-based suggestions for alterations during work-related, \nmusical\
\ performance, or sporting activities. \nUse of the NIH framework of behavioral\
\ study designs provided \nopportunity to examine the progression of development\
\ and bene- \nfits and measurement used in ergonomic studies. 25 \nDirect measurements\
\ to confirm the effectiveness and accuracy \nof implemented ergonomic adaptations\
\ were found in stage 0 \nand stage 1 studies. In stage 0, the basic science and\
\ proof of \nprinciple stage, intervention development consistently included \n\
measurements of motion and/or muscle activation measurements. \nVisualizations\
\ of ergonomic design implementations or visual- \nizations of exercises applied\
\ in the work setting were shown in"
- "47. Ljung BO , Forsgren S , Fridén J . Substance P and calcitonin gene-related\
\ peptide \nexpression at the extensor carpi radialis brevis muscle origin: implications\
\ for \nthe etiology of tennis elbow. J Orthop Res . 1999;17:554–559 . \n48. Alfredson\
\ H , Ljung BO , Thorsen K , Lorentzon R . In vivo investigation of ECRB \ntendons\
\ with microdialysis technique–no signs of inflammation but high \namounts of glutamate\
\ in tennis elbow. Acta Orthop Scand . 20 0 0;71:475–479 . \n49. Nirschl RP .\
\ Lateral epicondylitis/tendinosis. In: Morrey BF, Sanchez-Sotelo J, \nMorrey\
\ ME, eds. The Elbow and Its Disorders Elsevier; 2018:574–581 . \n50. Cha YK ,\
\ Kim SJ , Park NH , Kim JY , Kim JH , Park JY . Magnetic resonance"
- "shoulder, 21 of 88 elbow (19 lateral \nepicondylitis, 2 medial epicondylitis),\
\ 19 of \n88 wrist, 13 of 88 fingers 51.1% could not \nreturn to the same job \n\
Tool: intolerance to vibration still present \nafter 4 Y, especially for women\
\ \nErgonomic factors: poor seating, reaching, \npostures, and bad tool design\
\ \nInterventions: decreased incidence rate of \nLE from 2.1 to 0.1 \nSmith 2001\
\ Demonstrate the impact of a \nconcern by one worker to \nraise awareness of\
\ \ndepartmental problems, \nleading to general packaging \nimprovements made\
\ by the \nsupplier of seals of vacutainer \nneedles \nCase report \nA phlebotomist\
\ with LE due to \nforceful gripping and \nrepetitive twisting of seal on \nvacutainer\
\ needles \nRest: temporary rest from job"
- source_sentence: 1. What were the effects of exercise compared to ergonomics on
pain levels in the shoulder, elbows, and hand/wrists?
sentences:
- "palpation, maximal muscle \nstrength of arm and hand, \nfunction of arm and hand,\
\ \nDisability of the Arm, Shoulder, \nand Hand questionnaire \nPain decreased\
\ more in the exercise versus \nthe ergonomics group in the shoulder, \nelbows\
\ and hand/wrists. The DASH scores \nincreased in the ergonomics group, but \n\
decreased in the strengthening group. \nNumber of subjects with improved results\
\ \nwere significantly higher in the exercise \ngroup as compared to the ergonomics\
\ \ngroup. \nResults can be generalized to adults with \nupper limb chronic pain\
\ exposed to highly \nrepetitive and forceful manual work \nSoler-Font 2019 Compare\
\ effects of 3 types of \nprevention interventions on \npain and work functioning\
\ \nbetween nursing and control \ngroup"
- "pose, study design and methods, study sample, intervention or ex- \nposures (when\
\ appropriate), type of measurement and results or \noutcomes. The detailed information\
\ in Tables 1 through 4 are listed \nin the appendix. A synthesis table, Table\
\ 5, was constructed to in- \ntegrate the biomechanical and population exposure\
\ results into a \nset of defined harm reducing recommendations, and is presented\
\ \nin the results section. \nResults \nTissue involvement and tissue-level interventions\
\ \nAn anatomical landscape of the lateral elbow \nAn in-depth description of\
\ elbow anatomy is provided by Mor- \nrey, 29 who describes the architecture of\
\ the osteology, elbow joint \nstructures including their joint capsules and ligaments,\
\ bursae, ves-"
- "strength, and functional scores at 6 months when compared to use \nof a wrist\
\ orthosis and corticosteroid injections. 84 \nVarious types of interventions\
\ are used to alter or lessen the \nload on impacted tissues. The benefit of unloading\
\ or alteration of \nthe area of loading is based on the hypothesis that loading\
\ of a \npainful tendon perpetuates nociceptive stimuli, and that the sec- \n\
ondary hyperalgesia in tendinopathy is a response to ongoing no- \nciception.\
\ 38 Counterforce bracing has been shown to alter force"
- source_sentence: 1. What are the differences in strength and efficiency between
the ECRB and ECRL muscles during isometric and dynamic conditions?
sentences:
- "about twice as great for the ECRB compared to \nthe ECRL muscle. \nECRB and ECRL\
\ are efficient synergists; the ECRB \nis stronger isometrically, the ECRL becomes\
\ the \nstronger muscle as angular velocity increases. The \nsynergy of the ECRB\
\ and ECRL takes 30% less \nmass in comparison to if one muscle would \ngenerate\
\ the force. \nLjung 1999 To measure sarcomere length change in \nthe ECRB muscle\
\ during ulnar deviation \nwith the wrist in both the neutral and \npronated position.\
\ \nRepeated measures of sarcomere length \nchanges in 4 conditions: (1) wrist\
\ in \nneutral (in radial-ulnar and forearm in \nneutral rotation), (2) forearm\
\ neutral \nrotation + wrist in ulnar deviation, (3) \nwrist in neutral + forearm\
\ in pronation,"
- "oping LE. A high exposure (RSI > 5), older age, and self-perceived \npoor general\
\ health were associated with incidence of LE. 133"
- "lessen excursion of pronation and supination. For \ninstance, change tennis technique\
\ to balance foot \nsupport, trunk rotation, and arm contribution to the \nforce\
\ application to the racket, instead of getting the \nforce and speed mostly out\
\ of the arm. \nHigher force production takes place during \neccentric contractions\
\ for ECRB and ECRL. \nRepetitive movement with eccentric wrist \nextensor contractions\
\ \n• Avoid or modify activities that require full-length \nweighted stretch of\
\ ECRL and ECRB. \n• Seek design solutions so that objects do not need to \nbe\
\ lowered manually at a fast rate. \nThe synergy of the ECRL and ERCB is a useful\
\ \nmechanism for optimal function at higher \nangular velocities. \nHigh rapid\
\ rate of force development,"
- source_sentence: 1. What type of trial was conducted to compare corticoid and saline
treatments in the study published in the Am J Sports Med in 2013?
sentences:
- "lessen excursion of pronation and supination. For \ninstance, change tennis technique\
\ to balance foot \nsupport, trunk rotation, and arm contribution to the \nforce\
\ application to the racket, instead of getting the \nforce and speed mostly out\
\ of the arm. \nHigher force production takes place during \neccentric contractions\
\ for ECRB and ECRL. \nRepetitive movement with eccentric wrist \nextensor contractions\
\ \n• Avoid or modify activities that require full-length \nweighted stretch of\
\ ECRL and ECRB. \n• Seek design solutions so that objects do not need to \nbe\
\ lowered manually at a fast rate. \nThe synergy of the ECRL and ERCB is a useful\
\ \nmechanism for optimal function at higher \nangular velocities. \nHigh rapid\
\ rate of force development,"
- "histology \nControls: tendon with tendinous plate (aponeurosis) \nExtensor carpi\
\ radialis brevis (ECRB) degeneration \noccurs with age > 50 (not the peak years\
\ for LE), \ntherefore age > 50 not a factor for LE \nLE: edema in aponeurotic\
\ tissues, underneath \naponeurosis granulation tissue, with fibrosis, and \nfree\
\ nerve endings (pain), hypervascularization of \nthe aponeurosis \nLjung Forsgren,\
\ Friden \n1990 \nTo describe substance P and calcitonin \ngene-related peptide\
\ (CGRP) in \npatients with LE and healthy \nsubjects \nCross-sectional cohort\
\ comparison of 6 \npatients intra-operatively, and biopsies \nof 6 healthy volunteers\
\ \nSpecimens from patients included area \nclose to the bone, but area close\
\ to the \nbone was not included in healthy \nsubjects"
- "corticoid, or saline: a randomized, double-blind, placebo-controlled trial. Am\
\ J \nSports Med . 2013;41:625–635 . \n193. Houck DA , Kraeutler MJ , Thornton\
\ LB , McCarty EC , Bravman JT .T r e a t m e n t of \nlateral epicondylitis with\
\ autologous blood, platelet-rich plasma, or corticos- \nteroid injections: a\
\ systematic review of overlapping meta-analyses. Orthop J \nSports Med . 2019;7\
\ . \n194. Li A , Wang H , Yu Z , et al. Platelet-rich plasma vs corticosteroids\
\ for elbow \nepicondylitis: A systematic review and meta-analysis. Medicine (Baltimore)\
\ . \n2019;98:e18358 . \n195. Arirachakaran A , Sukthuayat A , Sisayanarane T\
\ , Laoratanavoraphong S , Kan- \nchanatawan W , Kongtharvonskul J . Platelet-rich\
\ plasma versus autologous"
- source_sentence: 1. What was the focus of the pilot study mentioned regarding tendinosis
of the Achilles tendon?
sentences:
- "C.W. Stegink-Jansen, J.G. Bynum, A.L. Lambropoulos et al. / Journal of Hand Therapy\
\ 34 (2021) 263–297 285 \nTable 3 ( continued ) \nAUTHOR/YEAR PURPOSE DESIGN SUBJECTS\
\ EXPOSURES EXPOSURE MEASUREMENTS RESULTS \nHerquelot et al. 2013b \nScand J Work\
\ Environ \nHealth 39(6):578-588 \nEstimate association between \noccupational\
\ risk factors and \nincidence of LE \nCohort study (cross-sectional and \nincidence)\
\ \n3710 workers; 1046 completed \nfollow-up \nRepetition, physical exertion,\
\ arm \nmovements \nWorker: health assessment \nHazards in work: self-reported\
\ \nRepetitive tasks and high physical \nexertion with elbow movements \ncontributed\
\ to incidence of LE \nNordander et al. 2013 Explore relationships between \n\
occupational risk factors and"
- "179. Dick FD , Graveling RA , Munro W , Walker-Bone K . Workplace management\
\ of \nupper limb disorders: a systematic review. Occup Med (Lond) . 2011;61:19–25\
\ . \n180. Buchanan H , Van Niekerk L , Grimmer K . Work transition after hand\
\ injury: a \nscoping review. J Hand Ther . 2020 . \n181. Rost KA , Alvero AM\
\ . Participatory approaches to workplace safety man- \nagement: bridging the\
\ gap between behavioral safety and participatory er- \ngonomics. Int J Occup\
\ Saf Ergon . 2020;26:194–203 . \n182. Bernardes JM , Ruiz-Frutos C , Moro ARP\
\ , Dias A . A low-cost and efficient par- \nticipatory ergonomic intervention to\
\ reduce the burden of work-related mus- \nculoskeletal disorders in an industrially\
\ developing country: an experience re-"
- "tendinosis of the Achilles tendon: a pilot study. AJR Am J Roentgenol . \n2007;189:W215–W220\
\ . \n74. van Leeuwen WF , Janssen SJ , Ring D , Chen N . Incidental magnetic\
\ resonance \nimaging signal changes in the extensor carpi radialis brevis origin\
\ are more \ncommon with age. J Shoulder Elbow Surg . 2016;25:1175–1181 . \n75.\
\ Rabago D , Lee KS , Ryan M , et al. Hypertonic dextrose and morrhuate sodium\
\ \ninjections (prolotherapy) for lateral epicondylosis (tennis elbow): results\
\ of a \nsingle-blind, pilot-level, randomized controlled trial. Am J Phys Med\
\ Rehabil . \n2013;92:587–596 . \n76. Scarpone M , Rabago DP , Zgierska A , Arbogast\
\ G , Snell E . The efficacy \nof prolotherapy for lateral epicondylosis: a pilot\
\ study. Clin J Sport Med ."
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9545454545454546
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9545454545454546
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.33333333333333326
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.20000000000000007
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000003
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9545454545454546
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9832240797077936
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9772727272727273
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9772727272727273
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). 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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l)
- **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: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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()
)
```
## 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("shivXy/ot-midterm-v0")
# Run inference
sentences = [
'1. What was the focus of the pilot study mentioned regarding tendinosis of the Achilles tendon?',
'tendinosis of the Achilles tendon: a pilot study. AJR Am J Roentgenol . \n2007;189:W215–W220 . \n74. van Leeuwen WF , Janssen SJ , Ring D , Chen N . Incidental magnetic resonance \nimaging signal changes in the extensor carpi radialis brevis origin are more \ncommon with age. J Shoulder Elbow Surg . 2016;25:1175–1181 . \n75. Rabago D , Lee KS , Ryan M , et al. Hypertonic dextrose and morrhuate sodium \ninjections (prolotherapy) for lateral epicondylosis (tennis elbow): results of a \nsingle-blind, pilot-level, randomized controlled trial. Am J Phys Med Rehabil . \n2013;92:587–596 . \n76. Scarpone M , Rabago DP , Zgierska A , Arbogast G , Snell E . The efficacy \nof prolotherapy for lateral epicondylosis: a pilot study. Clin J Sport Med .',
'179. Dick FD , Graveling RA , Munro W , Walker-Bone K . Workplace management of \nupper limb disorders: a systematic review. Occup Med (Lond) . 2011;61:19–25 . \n180. Buchanan H , Van Niekerk L , Grimmer K . Work transition after hand injury: a \nscoping review. J Hand Ther . 2020 . \n181. Rost KA , Alvero AM . Participatory approaches to workplace safety man- \nagement: bridging the gap between behavioral safety and participatory er- \ngonomics. Int J Occup Saf Ergon . 2020;26:194–203 . \n182. Bernardes JM , Ruiz-Frutos C , Moro ARP , Dias A . A low-cost and efficient par- \nticipatory ergonomic intervention to reduce the burden of work-related mus- \nculoskeletal disorders in an industrially developing country: an experience re-',
]
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]
```
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9545 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.9545 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.9545 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9832** |
| cosine_mrr@10 | 0.9773 |
| cosine_map@100 | 0.9773 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 812 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 812 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 12 tokens
- mean: 24.85 tokens
- max: 60 tokens
| - min: 8 tokens
- mean: 158.41 tokens
- max: 320 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2. What type of intervention is being compared to strength training in the study protocol by Sundstrup E and colleagues? | ment for work-related lateral epicondylitis. Work . 2010;37:81–86 .
161. Parimalam P , Premalatha MR , Padmini DS , Ganguli AK . Participatory er-
gonomics in redesigning a dyeing tub for fabric dyers. Work . 2012;43:453–458 .
162. Harari D , Casarotto RA . Effectiveness of a multifaceted intervention to manage
musculoskeletal disorders in workers of a medium-sized company. Int J Occup
Saf Ergon . 2021;27:247–257 .
163. Sundstrup E , Jakobsen MD , Andersen CH , et al. Participatory ergonomic inter-
vention versus strength training on chronic pain and work disability in slaugh-
terhouse workers: study protocol for a single-blind, randomized controlled
trial. BMC Musculoskelet Disord . 2013;14:67 . |
| 2. What does the increased signal intensity in the proximal portion of the lateral collateral ligament suggest about the patient's condition? | 266 C.W. Stegink-Jansen, J.G. Bynum, A.L. Lambropoulos et al. / Journal of Hand Therapy 34 (2021) 263–297
Fig. 3. Pathology. A 60-year-old female with right elbow pain for 5 weeks. (A) Coronal fat-suppressed FSE T2-weighted image showing mild thickening of the proximal
portion of the common extensor tendon with increased signal intensity (arrow), suggesting mild injury. Irregular thickening with increased signal intensity in the proximal
portion of the lateral collateral ligament (arrowhead) is also noted, suggesting mild injury. (B) and (C) Coronal PD FSE image and oblique radiograph showing cortical |
| 2. What factors are assessed in relation to lower extremity (LE) issues according to the systematic review? | Manufacturing:
• Electronics
• Auto parts
• Windows
• Cabinets
• Medical equipment
• Fitness equipment
Healthcare (excluding direct
patient care):
• Hospitals
• Health research
Worker: structured interviews,
physical examinations
Environment: workplace walk
through
Hazards in work: individual
assessments of biomechanical and
psychosocial factors
LE related to frequency of forceful
exertions or forearm supination and
forceful lifting; increased odds of LE
related to being age 36-50, female,
or a smoker; high social support
appeared protective against LE
van Rijn et al. 2009 Assess relationship between
work-related physical factors,
psychosocial factors, and LE
Systematic review
13 studies:
• 9 cross-sectional |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `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`: steps
- `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`: None
- `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