interstellar-ice-crystal-xs
This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-xs. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This was a proof-of-method model: it was created to show the applicability of some techniques to a certain dataset. It is not, however, really an improvement on the base model, and I advise against using in production.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: Snowflake/snowflake-arctic-embed-xs
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Training Dataset: scraped astronomy papers at the NLP for Space Science workshop.
- Language: en
- License: apache-2.0
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: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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:
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("SimoneAstarita/interstellar-ice-crystal-xs")
# Run inference
sentences = [
'New higher resolution images and our parametric modelling confirmed this finding.',
'New higher resolution images and our parametric modelling confirmed this finding.',
'Pan & Schlichting, 2012) and thus could slightly affect the surface density slope.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Training Dataset
The dataset is made of scrapes papers in astronomy, including abstract, introduction and conclusions. They are divided into sentences using nklt. We then duplicate them and train using the same senrence for positive and anchor. We are using SimSCE.
Unnamed Dataset
- Size: 416,298 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 1000 samples:
anchor positive type string string details - min: 4 tokens
- mean: 42.81 tokens
- max: 512 tokens
- min: 4 tokens
- mean: 42.81 tokens
- max: 512 tokens
- Samples:
anchor positive Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope
Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope
The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).
The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).
The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).
The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 32per_device_eval_batch_size
: 32warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss |
---|---|---|
0.0077 | 100 | 0.0025 |
0.0154 | 200 | 0.0032 |
0.0231 | 300 | 0.0026 |
0.0307 | 400 | 0.0026 |
0.0384 | 500 | 0.0041 |
0.0461 | 600 | 0.0014 |
0.0538 | 700 | 0.0019 |
0.0615 | 800 | 0.0015 |
0.0692 | 900 | 0.001 |
0.0769 | 1000 | 0.0005 |
0.0846 | 1100 | 0.0004 |
0.0922 | 1200 | 0.0013 |
0.0999 | 1300 | 0.0013 |
0.1076 | 1400 | 0.0027 |
0.1153 | 1500 | 0.0018 |
0.1230 | 1600 | 0.001 |
0.1307 | 1700 | 0.0014 |
0.1384 | 1800 | 0.0012 |
0.1460 | 1900 | 0.0041 |
0.1537 | 2000 | 0.0009 |
0.1614 | 2100 | 0.0005 |
0.1691 | 2200 | 0.0011 |
0.1768 | 2300 | 0.001 |
0.1845 | 2400 | 0.0004 |
0.1922 | 2500 | 0.0011 |
0.1998 | 2600 | 0.0044 |
0.2075 | 2700 | 0.0004 |
0.2152 | 2800 | 0.0022 |
0.2229 | 2900 | 0.0007 |
0.2306 | 3000 | 0.0006 |
0.2383 | 3100 | 0.0002 |
0.2460 | 3200 | 0.0006 |
0.2537 | 3300 | 0.0004 |
0.2613 | 3400 | 0.0013 |
0.2690 | 3500 | 0.0006 |
0.2767 | 3600 | 0.0005 |
0.2844 | 3700 | 0.0018 |
0.2921 | 3800 | 0.0023 |
0.2998 | 3900 | 0.0011 |
0.3075 | 4000 | 0.0007 |
0.3151 | 4100 | 0.0008 |
0.3228 | 4200 | 0.0013 |
0.3305 | 4300 | 0.0012 |
0.3382 | 4400 | 0.001 |
0.3459 | 4500 | 0.0016 |
0.3536 | 4600 | 0.0025 |
0.3613 | 4700 | 0.0015 |
0.3689 | 4800 | 0.0018 |
0.3766 | 4900 | 0.0019 |
0.3843 | 5000 | 0.0021 |
0.3920 | 5100 | 0.0018 |
0.3997 | 5200 | 0.0004 |
0.4074 | 5300 | 0.0006 |
0.4151 | 5400 | 0.0007 |
0.4228 | 5500 | 0.0009 |
0.4304 | 5600 | 0.0004 |
0.4381 | 5700 | 0.0003 |
0.4458 | 5800 | 0.0007 |
0.4535 | 5900 | 0.0013 |
0.4612 | 6000 | 0.0007 |
0.4689 | 6100 | 0.0005 |
0.4766 | 6200 | 0.001 |
0.4842 | 6300 | 0.0027 |
0.4919 | 6400 | 0.0018 |
0.4996 | 6500 | 0.0006 |
0.5073 | 6600 | 0.0008 |
0.5150 | 6700 | 0.0006 |
0.5227 | 6800 | 0.0007 |
0.5304 | 6900 | 0.001 |
0.5380 | 7000 | 0.0007 |
0.5457 | 7100 | 0.0005 |
0.5534 | 7200 | 0.0012 |
0.5611 | 7300 | 0.0012 |
0.5688 | 7400 | 0.0011 |
0.5765 | 7500 | 0.0005 |
0.5842 | 7600 | 0.0013 |
0.5919 | 7700 | 0.0012 |
0.5995 | 7800 | 0.0007 |
0.6072 | 7900 | 0.0012 |
0.6149 | 8000 | 0.0012 |
0.6226 | 8100 | 0.0003 |
0.6303 | 8200 | 0.0003 |
0.6380 | 8300 | 0.0003 |
0.6457 | 8400 | 0.002 |
0.6533 | 8500 | 0.0003 |
0.6610 | 8600 | 0.0016 |
0.6687 | 8700 | 0.0003 |
0.6764 | 8800 | 0.0002 |
0.6841 | 8900 | 0.0006 |
0.6918 | 9000 | 0.0005 |
0.6995 | 9100 | 0.0017 |
0.7071 | 9200 | 0.0037 |
0.7148 | 9300 | 0.0005 |
0.7225 | 9400 | 0.0006 |
0.7302 | 9500 | 0.0004 |
0.7379 | 9600 | 0.0002 |
0.7456 | 9700 | 0.0008 |
0.7533 | 9800 | 0.0005 |
0.7610 | 9900 | 0.0006 |
0.7686 | 10000 | 0.0004 |
0.7763 | 10100 | 0.0004 |
0.7840 | 10200 | 0.0006 |
0.7917 | 10300 | 0.0019 |
0.7994 | 10400 | 0.0007 |
0.8071 | 10500 | 0.0003 |
0.8148 | 10600 | 0.0003 |
0.8224 | 10700 | 0.0005 |
0.8301 | 10800 | 0.0009 |
0.8378 | 10900 | 0.0006 |
0.8455 | 11000 | 0.002 |
0.8532 | 11100 | 0.0018 |
0.8609 | 11200 | 0.0009 |
0.8686 | 11300 | 0.0004 |
0.8762 | 11400 | 0.0005 |
0.8839 | 11500 | 0.0008 |
0.8916 | 11600 | 0.0003 |
0.8993 | 11700 | 0.0002 |
0.9070 | 11800 | 0.0004 |
0.9147 | 11900 | 0.0007 |
0.9224 | 12000 | 0.0009 |
0.9301 | 12100 | 0.0007 |
0.9377 | 12200 | 0.0007 |
0.9454 | 12300 | 0.0009 |
0.9531 | 12400 | 0.0007 |
0.9608 | 12500 | 0.0009 |
0.9685 | 12600 | 0.0004 |
0.9762 | 12700 | 0.0002 |
0.9839 | 12800 | 0.0003 |
0.9915 | 12900 | 0.0002 |
0.9992 | 13000 | 0.0002 |
1.0069 | 13100 | 0.0006 |
1.0146 | 13200 | 0.0007 |
1.0223 | 13300 | 0.0007 |
1.0300 | 13400 | 0.0005 |
1.0377 | 13500 | 0.0008 |
1.0453 | 13600 | 0.0016 |
1.0530 | 13700 | 0.0007 |
1.0607 | 13800 | 0.0013 |
1.0684 | 13900 | 0.0005 |
1.0761 | 14000 | 0.0002 |
1.0838 | 14100 | 0.0001 |
1.0915 | 14200 | 0.0003 |
1.0992 | 14300 | 0.0003 |
1.1068 | 14400 | 0.0006 |
1.1145 | 14500 | 0.0002 |
1.1222 | 14600 | 0.0003 |
1.1299 | 14700 | 0.0002 |
1.1376 | 14800 | 0.0006 |
1.1453 | 14900 | 0.0011 |
1.1530 | 15000 | 0.0004 |
1.1606 | 15100 | 0.0001 |
1.1683 | 15200 | 0.0003 |
1.1760 | 15300 | 0.0001 |
1.1837 | 15400 | 0.0002 |
1.1914 | 15500 | 0.0001 |
1.1991 | 15600 | 0.003 |
1.2068 | 15700 | 0.0001 |
1.2145 | 15800 | 0.0002 |
1.2221 | 15900 | 0.0005 |
1.2298 | 16000 | 0.0004 |
1.2375 | 16100 | 0.0001 |
1.2452 | 16200 | 0.0003 |
1.2529 | 16300 | 0.0003 |
1.2606 | 16400 | 0.0008 |
1.2683 | 16500 | 0.0004 |
1.2759 | 16600 | 0.0001 |
1.2836 | 16700 | 0.0002 |
1.2913 | 16800 | 0.0011 |
1.2990 | 16900 | 0.0001 |
1.3067 | 17000 | 0.0001 |
1.3144 | 17100 | 0.0002 |
1.3221 | 17200 | 0.0005 |
1.3297 | 17300 | 0.0012 |
1.3374 | 17400 | 0.0003 |
1.3451 | 17500 | 0.0002 |
1.3528 | 17600 | 0.0009 |
1.3605 | 17700 | 0.0003 |
1.3682 | 17800 | 0.0005 |
1.3759 | 17900 | 0.0008 |
1.3836 | 18000 | 0.0005 |
1.3912 | 18100 | 0.0007 |
1.3989 | 18200 | 0.0002 |
1.4066 | 18300 | 0.0003 |
1.4143 | 18400 | 0.0002 |
1.4220 | 18500 | 0.0001 |
1.4297 | 18600 | 0.0001 |
1.4374 | 18700 | 0.0001 |
1.4450 | 18800 | 0.0005 |
1.4527 | 18900 | 0.0002 |
1.4604 | 19000 | 0.0001 |
1.4681 | 19100 | 0.0002 |
1.4758 | 19200 | 0.0006 |
1.4835 | 19300 | 0.0015 |
1.4912 | 19400 | 0.0012 |
1.4988 | 19500 | 0.0003 |
1.5065 | 19600 | 0.0005 |
1.5142 | 19700 | 0.0001 |
1.5219 | 19800 | 0.0002 |
1.5296 | 19900 | 0.0009 |
1.5373 | 20000 | 0.0002 |
1.5450 | 20100 | 0.0001 |
1.5527 | 20200 | 0.0003 |
1.5603 | 20300 | 0.0006 |
1.5680 | 20400 | 0.0002 |
1.5757 | 20500 | 0.0004 |
1.5834 | 20600 | 0.0006 |
1.5911 | 20700 | 0.0004 |
1.5988 | 20800 | 0.0002 |
1.6065 | 20900 | 0.0006 |
1.6141 | 21000 | 0.0006 |
1.6218 | 21100 | 0.0001 |
1.6295 | 21200 | 0.0001 |
1.6372 | 21300 | 0.0001 |
1.6449 | 21400 | 0.0008 |
1.6526 | 21500 | 0.0001 |
1.6603 | 21600 | 0.0005 |
1.6679 | 21700 | 0.0001 |
1.6756 | 21800 | 0.0001 |
1.6833 | 21900 | 0.0001 |
1.6910 | 22000 | 0.0001 |
1.6987 | 22100 | 0.0008 |
1.7064 | 22200 | 0.0014 |
1.7141 | 22300 | 0.0002 |
1.7218 | 22400 | 0.0007 |
1.7294 | 22500 | 0.0001 |
1.7371 | 22600 | 0.0001 |
1.7448 | 22700 | 0.0001 |
1.7525 | 22800 | 0.0002 |
1.7602 | 22900 | 0.0002 |
1.7679 | 23000 | 0.0001 |
1.7756 | 23100 | 0.0001 |
1.7832 | 23200 | 0.0005 |
1.7909 | 23300 | 0.0004 |
1.7986 | 23400 | 0.0002 |
1.8063 | 23500 | 0.0001 |
1.8140 | 23600 | 0.0001 |
1.8217 | 23700 | 0.0001 |
1.8294 | 23800 | 0.0004 |
1.8370 | 23900 | 0.0002 |
1.8447 | 24000 | 0.0002 |
1.8524 | 24100 | 0.0013 |
1.8601 | 24200 | 0.0004 |
1.8678 | 24300 | 0.0002 |
1.8755 | 24400 | 0.0002 |
1.8832 | 24500 | 0.0001 |
1.8909 | 24600 | 0.0001 |
1.8985 | 24700 | 0.0001 |
1.9062 | 24800 | 0.0002 |
1.9139 | 24900 | 0.0005 |
1.9216 | 25000 | 0.0001 |
1.9293 | 25100 | 0.0001 |
1.9370 | 25200 | 0.0002 |
1.9447 | 25300 | 0.0002 |
1.9523 | 25400 | 0.0006 |
1.9600 | 25500 | 0.0004 |
1.9677 | 25600 | 0.0002 |
1.9754 | 25700 | 0.0001 |
1.9831 | 25800 | 0.0001 |
1.9908 | 25900 | 0.0001 |
1.9985 | 26000 | 0.0001 |
2.0061 | 26100 | 0.0002 |
2.0138 | 26200 | 0.0007 |
2.0215 | 26300 | 0.0003 |
2.0292 | 26400 | 0.0001 |
2.0369 | 26500 | 0.0011 |
2.0446 | 26600 | 0.0002 |
2.0523 | 26700 | 0.0001 |
2.0600 | 26800 | 0.0002 |
2.0676 | 26900 | 0.0004 |
2.0753 | 27000 | 0.0001 |
2.0830 | 27100 | 0.0001 |
2.0907 | 27200 | 0.0001 |
2.0984 | 27300 | 0.0002 |
2.1061 | 27400 | 0.0001 |
2.1138 | 27500 | 0.0001 |
2.1214 | 27600 | 0.0001 |
2.1291 | 27700 | 0.0001 |
2.1368 | 27800 | 0.0003 |
2.1445 | 27900 | 0.0012 |
2.1522 | 28000 | 0.0001 |
2.1599 | 28100 | 0.0001 |
2.1676 | 28200 | 0.0001 |
2.1752 | 28300 | 0.0001 |
2.1829 | 28400 | 0.0001 |
2.1906 | 28500 | 0.0001 |
2.1983 | 28600 | 0.0014 |
2.2060 | 28700 | 0.0001 |
2.2137 | 28800 | 0.0001 |
2.2214 | 28900 | 0.0002 |
2.2291 | 29000 | 0.0 |
2.2367 | 29100 | 0.0001 |
2.2444 | 29200 | 0.0001 |
2.2521 | 29300 | 0.0001 |
2.2598 | 29400 | 0.0001 |
2.2675 | 29500 | 0.0001 |
2.2752 | 29600 | 0.0001 |
2.2829 | 29700 | 0.0001 |
2.2905 | 29800 | 0.0001 |
2.2982 | 29900 | 0.0001 |
2.3059 | 30000 | 0.0001 |
2.3136 | 30100 | 0.0001 |
2.3213 | 30200 | 0.0002 |
2.3290 | 30300 | 0.0011 |
2.3367 | 30400 | 0.0001 |
2.3444 | 30500 | 0.0001 |
2.3520 | 30600 | 0.0005 |
2.3597 | 30700 | 0.0001 |
2.3674 | 30800 | 0.0001 |
2.3751 | 30900 | 0.0006 |
2.3828 | 31000 | 0.0001 |
2.3905 | 31100 | 0.0001 |
2.3982 | 31200 | 0.0002 |
2.4058 | 31300 | 0.0001 |
2.4135 | 31400 | 0.0001 |
2.4212 | 31500 | 0.0001 |
2.4289 | 31600 | 0.0001 |
2.4366 | 31700 | 0.0001 |
2.4443 | 31800 | 0.0004 |
2.4520 | 31900 | 0.0001 |
2.4596 | 32000 | 0.0001 |
2.4673 | 32100 | 0.0002 |
2.4750 | 32200 | 0.0002 |
2.4827 | 32300 | 0.0004 |
2.4904 | 32400 | 0.0008 |
2.4981 | 32500 | 0.0001 |
2.5058 | 32600 | 0.0001 |
2.5135 | 32700 | 0.0001 |
2.5211 | 32800 | 0.0001 |
2.5288 | 32900 | 0.0006 |
2.5365 | 33000 | 0.0001 |
2.5442 | 33100 | 0.0001 |
2.5519 | 33200 | 0.0002 |
2.5596 | 33300 | 0.0001 |
2.5673 | 33400 | 0.0002 |
2.5749 | 33500 | 0.0001 |
2.5826 | 33600 | 0.0001 |
2.5903 | 33700 | 0.0001 |
2.5980 | 33800 | 0.0001 |
2.6057 | 33900 | 0.0001 |
2.6134 | 34000 | 0.0007 |
2.6211 | 34100 | 0.0 |
2.6287 | 34200 | 0.0001 |
2.6364 | 34300 | 0.0001 |
2.6441 | 34400 | 0.0006 |
2.6518 | 34500 | 0.0001 |
2.6595 | 34600 | 0.0001 |
2.6672 | 34700 | 0.0001 |
2.6749 | 34800 | 0.0 |
2.6826 | 34900 | 0.0001 |
2.6902 | 35000 | 0.0001 |
2.6979 | 35100 | 0.0005 |
2.7056 | 35200 | 0.0006 |
2.7133 | 35300 | 0.0001 |
2.7210 | 35400 | 0.0005 |
2.7287 | 35500 | 0.0001 |
2.7364 | 35600 | 0.0001 |
2.7440 | 35700 | 0.0001 |
2.7517 | 35800 | 0.0001 |
2.7594 | 35900 | 0.0001 |
2.7671 | 36000 | 0.0001 |
2.7748 | 36100 | 0.0001 |
2.7825 | 36200 | 0.0005 |
2.7902 | 36300 | 0.0001 |
2.7978 | 36400 | 0.0001 |
2.8055 | 36500 | 0.0001 |
2.8132 | 36600 | 0.0001 |
2.8209 | 36700 | 0.0001 |
2.8286 | 36800 | 0.0001 |
2.8363 | 36900 | 0.0001 |
2.8440 | 37000 | 0.0001 |
2.8517 | 37100 | 0.0013 |
2.8593 | 37200 | 0.0001 |
2.8670 | 37300 | 0.0001 |
2.8747 | 37400 | 0.0001 |
2.8824 | 37500 | 0.0001 |
2.8901 | 37600 | 0.0001 |
2.8978 | 37700 | 0.0001 |
2.9055 | 37800 | 0.0001 |
2.9131 | 37900 | 0.0002 |
2.9208 | 38000 | 0.0001 |
2.9285 | 38100 | 0.0001 |
2.9362 | 38200 | 0.0001 |
2.9439 | 38300 | 0.0001 |
2.9516 | 38400 | 0.0004 |
2.9593 | 38500 | 0.0001 |
2.9669 | 38600 | 0.0001 |
2.9746 | 38700 | 0.0001 |
2.9823 | 38800 | 0.0001 |
2.9900 | 38900 | 0.0001 |
2.9977 | 39000 | 0.0001 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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Model tree for SimoneAstarita/interstellar-ice-crystal-xs
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