SentenceTransformer based on FacebookAI/xlm-roberta-base
This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base on the en-es 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: FacebookAI/xlm-roberta-base
- Maximum Sequence Length: 128 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
- Languages: en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh
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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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("vallabh001/xlm-roberta-base-multilingual-en-es")
# Run inference
sentences = [
'We need a different machine.',
'Necesitamos una máquina diferente.',
'Entonces, ¿dónde nos deja esto?',
]
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
Knowledge Distillation
- Dataset:
en-es
- Evaluated with
MSEEvaluator
Metric | Value |
---|---|
negative_mse | -10.1836 |
Translation
- Dataset:
en-es
- Evaluated with
TranslationEvaluator
Metric | Value |
---|---|
src2trg_accuracy | 0.9879 |
trg2src_accuracy | 0.9909 |
mean_accuracy | 0.9894 |
Semantic Similarity
- Dataset:
sts17-es-en-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.7671 |
spearman_cosine | 0.7903 |
Training Details
Training Dataset
en-es
- Dataset: en-es at 0c70bc6
- Size: 404,981 training samples
- Columns:
english
,non_english
, andlabel
- Approximate statistics based on the first 1000 samples:
english non_english label type string string list details - min: 4 tokens
- mean: 25.77 tokens
- max: 128 tokens
- min: 4 tokens
- mean: 25.42 tokens
- max: 128 tokens
- size: 768 elements
- Samples:
english non_english label And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.
Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.
[-0.59398353099823, 0.9714106321334839, 0.6800687313079834, -0.21585586667060852, -0.7509507536888123, ...]
One thing I often ask about is ancient Greek and how this relates.
Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.
[-0.09777131676673889, 0.07093200832605362, -0.42989036440849304, -0.1457505226135254, 1.4382765293121338, ...]
See, the thing we're doing right now is we're forcing people to learn mathematics.
Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.
[0.39432215690612793, 0.1891053169965744, -0.3788300156593323, 0.438666433095932, 0.2727019190788269, ...]
- Loss:
MSELoss
Evaluation Dataset
en-es
- Dataset: en-es at 0c70bc6
- Size: 990 evaluation samples
- Columns:
english
,non_english
, andlabel
- Approximate statistics based on the first 990 samples:
english non_english label type string string list details - min: 4 tokens
- mean: 26.42 tokens
- max: 128 tokens
- min: 4 tokens
- mean: 26.47 tokens
- max: 128 tokens
- size: 768 elements
- Samples:
english non_english label Thank you so much, Chris.
Muchas gracias Chris.
[-0.43312570452690125, 1.0602686405181885, -0.07791059464216232, -0.41704198718070984, 1.676845908164978, ...]
And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.
Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.
[0.27005693316459656, 0.5391747951507568, -0.2580487132072449, -0.6613675951957703, 0.6738824248313904, ...]
I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.
He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche.
[-0.2532017230987549, 0.04791336879134178, -0.1317490190267563, -0.7357572913169861, 0.23663584887981415, ...]
- Loss:
MSELoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 64per_device_eval_batch_size
: 64learning_rate
: 2e-05num_train_epochs
: 5warmup_ratio
: 0.1bf16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_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
: Truefp16
: Falsefp16_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
: Falseinclude_for_metrics
: []eval_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
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | en-es loss | en-es_negative_mse | en-es_mean_accuracy | sts17-es-en-test_spearman_cosine |
---|---|---|---|---|---|---|
0.0158 | 100 | 0.6528 | - | - | - | - |
0.0316 | 200 | 0.5634 | - | - | - | - |
0.0474 | 300 | 0.4418 | - | - | - | - |
0.0632 | 400 | 0.3009 | - | - | - | - |
0.0790 | 500 | 0.2744 | - | - | - | - |
0.0948 | 600 | 0.2677 | - | - | - | - |
0.1106 | 700 | 0.2661 | - | - | - | - |
0.1264 | 800 | 0.2614 | - | - | - | - |
0.1422 | 900 | 0.2583 | - | - | - | - |
0.1580 | 1000 | 0.2582 | - | - | - | - |
0.1738 | 1100 | 0.2579 | - | - | - | - |
0.1896 | 1200 | 0.256 | - | - | - | - |
0.2054 | 1300 | 0.2511 | - | - | - | - |
0.2212 | 1400 | 0.2467 | - | - | - | - |
0.2370 | 1500 | 0.2423 | - | - | - | - |
0.2528 | 1600 | 0.2364 | - | - | - | - |
0.2686 | 1700 | 0.2305 | - | - | - | - |
0.2845 | 1800 | 0.2248 | - | - | - | - |
0.3003 | 1900 | 0.2184 | - | - | - | - |
0.3161 | 2000 | 0.2143 | - | - | - | - |
0.3319 | 2100 | 0.2098 | - | - | - | - |
0.3477 | 2200 | 0.2055 | - | - | - | - |
0.3635 | 2300 | 0.1999 | - | - | - | - |
0.3793 | 2400 | 0.1965 | - | - | - | - |
0.3951 | 2500 | 0.1919 | - | - | - | - |
0.4109 | 2600 | 0.1889 | - | - | - | - |
0.4267 | 2700 | 0.1858 | - | - | - | - |
0.4425 | 2800 | 0.1826 | - | - | - | - |
0.4583 | 2900 | 0.18 | - | - | - | - |
0.4741 | 3000 | 0.1774 | - | - | - | - |
0.4899 | 3100 | 0.1758 | - | - | - | - |
0.5057 | 3200 | 0.1738 | - | - | - | - |
0.5215 | 3300 | 0.1706 | - | - | - | - |
0.5373 | 3400 | 0.1678 | - | - | - | - |
0.5531 | 3500 | 0.1664 | - | - | - | - |
0.5689 | 3600 | 0.1647 | - | - | - | - |
0.5847 | 3700 | 0.163 | - | - | - | - |
0.6005 | 3800 | 0.1605 | - | - | - | - |
0.6163 | 3900 | 0.1594 | - | - | - | - |
0.6321 | 4000 | 0.1576 | - | - | - | - |
0.6479 | 4100 | 0.1561 | - | - | - | - |
0.6637 | 4200 | 0.1541 | - | - | - | - |
0.6795 | 4300 | 0.1545 | - | - | - | - |
0.6953 | 4400 | 0.1535 | - | - | - | - |
0.7111 | 4500 | 0.1523 | - | - | - | - |
0.7269 | 4600 | 0.1502 | - | - | - | - |
0.7427 | 4700 | 0.1487 | - | - | - | - |
0.7585 | 4800 | 0.1486 | - | - | - | - |
0.7743 | 4900 | 0.1477 | - | - | - | - |
0.7901 | 5000 | 0.1465 | 0.1390 | -14.681906 | 0.9803 | 0.6371 |
0.8059 | 5100 | 0.1469 | - | - | - | - |
0.8217 | 5200 | 0.1449 | - | - | - | - |
0.8375 | 5300 | 0.1437 | - | - | - | - |
0.8534 | 5400 | 0.142 | - | - | - | - |
0.8692 | 5500 | 0.1423 | - | - | - | - |
0.8850 | 5600 | 0.1424 | - | - | - | - |
0.9008 | 5700 | 0.1415 | - | - | - | - |
0.9166 | 5800 | 0.1407 | - | - | - | - |
0.9324 | 5900 | 0.1396 | - | - | - | - |
0.9482 | 6000 | 0.1388 | - | - | - | - |
0.9640 | 6100 | 0.1391 | - | - | - | - |
0.9798 | 6200 | 0.1368 | - | - | - | - |
0.9956 | 6300 | 0.1366 | - | - | - | - |
1.0114 | 6400 | 0.1367 | - | - | - | - |
1.0272 | 6500 | 0.1343 | - | - | - | - |
1.0430 | 6600 | 0.1341 | - | - | - | - |
1.0588 | 6700 | 0.1349 | - | - | - | - |
1.0746 | 6800 | 0.1327 | - | - | - | - |
1.0904 | 6900 | 0.1334 | - | - | - | - |
1.1062 | 7000 | 0.133 | - | - | - | - |
1.1220 | 7100 | 0.1316 | - | - | - | - |
1.1378 | 7200 | 0.1308 | - | - | - | - |
1.1536 | 7300 | 0.1316 | - | - | - | - |
1.1694 | 7400 | 0.1298 | - | - | - | - |
1.1852 | 7500 | 0.1294 | - | - | - | - |
1.2010 | 7600 | 0.1295 | - | - | - | - |
1.2168 | 7700 | 0.13 | - | - | - | - |
1.2326 | 7800 | 0.1285 | - | - | - | - |
1.2484 | 7900 | 0.1278 | - | - | - | - |
1.2642 | 8000 | 0.1272 | - | - | - | - |
1.2800 | 8100 | 0.1262 | - | - | - | - |
1.2958 | 8200 | 0.1275 | - | - | - | - |
1.3116 | 8300 | 0.1266 | - | - | - | - |
1.3274 | 8400 | 0.1252 | - | - | - | - |
1.3432 | 8500 | 0.1256 | - | - | - | - |
1.3590 | 8600 | 0.1246 | - | - | - | - |
1.3748 | 8700 | 0.1254 | - | - | - | - |
1.3906 | 8800 | 0.1242 | - | - | - | - |
1.4064 | 8900 | 0.1249 | - | - | - | - |
1.4223 | 9000 | 0.1233 | - | - | - | - |
1.4381 | 9100 | 0.1238 | - | - | - | - |
1.4539 | 9200 | 0.1231 | - | - | - | - |
1.4697 | 9300 | 0.122 | - | - | - | - |
1.4855 | 9400 | 0.1217 | - | - | - | - |
1.5013 | 9500 | 0.1225 | - | - | - | - |
1.5171 | 9600 | 0.1213 | - | - | - | - |
1.5329 | 9700 | 0.1208 | - | - | - | - |
1.5487 | 9800 | 0.1214 | - | - | - | - |
1.5645 | 9900 | 0.1205 | - | - | - | - |
1.5803 | 10000 | 0.12 | 0.1120 | -12.20076 | 0.9843 | 0.7137 |
1.5961 | 10100 | 0.1205 | - | - | - | - |
1.6119 | 10200 | 0.12 | - | - | - | - |
1.6277 | 10300 | 0.1187 | - | - | - | - |
1.6435 | 10400 | 0.1184 | - | - | - | - |
1.6593 | 10500 | 0.1178 | - | - | - | - |
1.6751 | 10600 | 0.1188 | - | - | - | - |
1.6909 | 10700 | 0.1184 | - | - | - | - |
1.7067 | 10800 | 0.1168 | - | - | - | - |
1.7225 | 10900 | 0.1175 | - | - | - | - |
1.7383 | 11000 | 0.1158 | - | - | - | - |
1.7541 | 11100 | 0.1159 | - | - | - | - |
1.7699 | 11200 | 0.1178 | - | - | - | - |
1.7857 | 11300 | 0.1158 | - | - | - | - |
1.8015 | 11400 | 0.1161 | - | - | - | - |
1.8173 | 11500 | 0.1151 | - | - | - | - |
1.8331 | 11600 | 0.1147 | - | - | - | - |
1.8489 | 11700 | 0.1152 | - | - | - | - |
1.8647 | 11800 | 0.1144 | - | - | - | - |
1.8805 | 11900 | 0.1145 | - | - | - | - |
1.8963 | 12000 | 0.1144 | - | - | - | - |
1.9121 | 12100 | 0.1139 | - | - | - | - |
1.9279 | 12200 | 0.1144 | - | - | - | - |
1.9437 | 12300 | 0.1144 | - | - | - | - |
1.9595 | 12400 | 0.1124 | - | - | - | - |
1.9753 | 12500 | 0.1134 | - | - | - | - |
1.9912 | 12600 | 0.1133 | - | - | - | - |
2.0070 | 12700 | 0.1125 | - | - | - | - |
2.0228 | 12800 | 0.1108 | - | - | - | - |
2.0386 | 12900 | 0.1112 | - | - | - | - |
2.0544 | 13000 | 0.1109 | - | - | - | - |
2.0702 | 13100 | 0.1105 | - | - | - | - |
2.0860 | 13200 | 0.1112 | - | - | - | - |
2.1018 | 13300 | 0.1105 | - | - | - | - |
2.1176 | 13400 | 0.1105 | - | - | - | - |
2.1334 | 13500 | 0.11 | - | - | - | - |
2.1492 | 13600 | 0.1096 | - | - | - | - |
2.1650 | 13700 | 0.1098 | - | - | - | - |
2.1808 | 13800 | 0.1093 | - | - | - | - |
2.1966 | 13900 | 0.1089 | - | - | - | - |
2.2124 | 14000 | 0.1091 | - | - | - | - |
2.2282 | 14100 | 0.1091 | - | - | - | - |
2.2440 | 14200 | 0.1086 | - | - | - | - |
2.2598 | 14300 | 0.1089 | - | - | - | - |
2.2756 | 14400 | 0.1087 | - | - | - | - |
2.2914 | 14500 | 0.1083 | - | - | - | - |
2.3072 | 14600 | 0.1091 | - | - | - | - |
2.3230 | 14700 | 0.1083 | - | - | - | - |
2.3388 | 14800 | 0.1088 | - | - | - | - |
2.3546 | 14900 | 0.1071 | - | - | - | - |
2.3704 | 15000 | 0.1085 | 0.1015 | -11.243325 | 0.9843 | 0.7625 |
2.3862 | 15100 | 0.1077 | - | - | - | - |
2.4020 | 15200 | 0.1076 | - | - | - | - |
2.4178 | 15300 | 0.108 | - | - | - | - |
2.4336 | 15400 | 0.1066 | - | - | - | - |
2.4494 | 15500 | 0.1062 | - | - | - | - |
2.4652 | 15600 | 0.1065 | - | - | - | - |
2.4810 | 15700 | 0.1058 | - | - | - | - |
2.4968 | 15800 | 0.1071 | - | - | - | - |
2.5126 | 15900 | 0.1071 | - | - | - | - |
2.5284 | 16000 | 0.1066 | - | - | - | - |
2.5442 | 16100 | 0.1067 | - | - | - | - |
2.5601 | 16200 | 0.1057 | - | - | - | - |
2.5759 | 16300 | 0.106 | - | - | - | - |
2.5917 | 16400 | 0.1061 | - | - | - | - |
2.6075 | 16500 | 0.1047 | - | - | - | - |
2.6233 | 16600 | 0.1057 | - | - | - | - |
2.6391 | 16700 | 0.106 | - | - | - | - |
2.6549 | 16800 | 0.1055 | - | - | - | - |
2.6707 | 16900 | 0.105 | - | - | - | - |
2.6865 | 17000 | 0.1047 | - | - | - | - |
2.7023 | 17100 | 0.1042 | - | - | - | - |
2.7181 | 17200 | 0.1057 | - | - | - | - |
2.7339 | 17300 | 0.1051 | - | - | - | - |
2.7497 | 17400 | 0.1055 | - | - | - | - |
2.7655 | 17500 | 0.1047 | - | - | - | - |
2.7813 | 17600 | 0.1043 | - | - | - | - |
2.7971 | 17700 | 0.1034 | - | - | - | - |
2.8129 | 17800 | 0.1039 | - | - | - | - |
2.8287 | 17900 | 0.1038 | - | - | - | - |
2.8445 | 18000 | 0.1032 | - | - | - | - |
2.8603 | 18100 | 0.103 | - | - | - | - |
2.8761 | 18200 | 0.1035 | - | - | - | - |
2.8919 | 18300 | 0.1024 | - | - | - | - |
2.9077 | 18400 | 0.1032 | - | - | - | - |
2.9235 | 18500 | 0.1031 | - | - | - | - |
2.9393 | 18600 | 0.1034 | - | - | - | - |
2.9551 | 18700 | 0.1033 | - | - | - | - |
2.9709 | 18800 | 0.1036 | - | - | - | - |
2.9867 | 18900 | 0.1029 | - | - | - | - |
3.0025 | 19000 | 0.1024 | - | - | - | - |
3.0183 | 19100 | 0.1017 | - | - | - | - |
3.0341 | 19200 | 0.1012 | - | - | - | - |
3.0499 | 19300 | 0.1016 | - | - | - | - |
3.0657 | 19400 | 0.1012 | - | - | - | - |
3.0815 | 19500 | 0.1009 | - | - | - | - |
3.0973 | 19600 | 0.1015 | - | - | - | - |
3.1131 | 19700 | 0.1014 | - | - | - | - |
3.1290 | 19800 | 0.1004 | - | - | - | - |
3.1448 | 19900 | 0.1011 | - | - | - | - |
3.1606 | 20000 | 0.1006 | 0.0952 | -10.662492 | 0.9879 | 0.7811 |
3.1764 | 20100 | 0.1007 | - | - | - | - |
3.1922 | 20200 | 0.1015 | - | - | - | - |
3.2080 | 20300 | 0.1005 | - | - | - | - |
3.2238 | 20400 | 0.1017 | - | - | - | - |
3.2396 | 20500 | 0.1012 | - | - | - | - |
3.2554 | 20600 | 0.0998 | - | - | - | - |
3.2712 | 20700 | 0.0997 | - | - | - | - |
3.2870 | 20800 | 0.1001 | - | - | - | - |
3.3028 | 20900 | 0.1009 | - | - | - | - |
3.3186 | 21000 | 0.1 | - | - | - | - |
3.3344 | 21100 | 0.1001 | - | - | - | - |
3.3502 | 21200 | 0.1008 | - | - | - | - |
3.3660 | 21300 | 0.0996 | - | - | - | - |
3.3818 | 21400 | 0.0993 | - | - | - | - |
3.3976 | 21500 | 0.1004 | - | - | - | - |
3.4134 | 21600 | 0.0996 | - | - | - | - |
3.4292 | 21700 | 0.0993 | - | - | - | - |
3.4450 | 21800 | 0.0997 | - | - | - | - |
3.4608 | 21900 | 0.0997 | - | - | - | - |
3.4766 | 22000 | 0.0997 | - | - | - | - |
3.4924 | 22100 | 0.0984 | - | - | - | - |
3.5082 | 22200 | 0.0999 | - | - | - | - |
3.5240 | 22300 | 0.099 | - | - | - | - |
3.5398 | 22400 | 0.0992 | - | - | - | - |
3.5556 | 22500 | 0.0988 | - | - | - | - |
3.5714 | 22600 | 0.0989 | - | - | - | - |
3.5872 | 22700 | 0.0989 | - | - | - | - |
3.6030 | 22800 | 0.0978 | - | - | - | - |
3.6188 | 22900 | 0.0987 | - | - | - | - |
3.6346 | 23000 | 0.0997 | - | - | - | - |
3.6504 | 23100 | 0.0994 | - | - | - | - |
3.6662 | 23200 | 0.0984 | - | - | - | - |
3.6820 | 23300 | 0.0985 | - | - | - | - |
3.6979 | 23400 | 0.0983 | - | - | - | - |
3.7137 | 23500 | 0.0992 | - | - | - | - |
3.7295 | 23600 | 0.0983 | - | - | - | - |
3.7453 | 23700 | 0.0987 | - | - | - | - |
3.7611 | 23800 | 0.0983 | - | - | - | - |
3.7769 | 23900 | 0.0969 | - | - | - | - |
3.7927 | 24000 | 0.0984 | - | - | - | - |
3.8085 | 24100 | 0.0976 | - | - | - | - |
3.8243 | 24200 | 0.0984 | - | - | - | - |
3.8401 | 24300 | 0.0974 | - | - | - | - |
3.8559 | 24400 | 0.0982 | - | - | - | - |
3.8717 | 24500 | 0.0983 | - | - | - | - |
3.8875 | 24600 | 0.0986 | - | - | - | - |
3.9033 | 24700 | 0.0977 | - | - | - | - |
3.9191 | 24800 | 0.0974 | - | - | - | - |
3.9349 | 24900 | 0.0979 | - | - | - | - |
3.9507 | 25000 | 0.0974 | 0.0916 | -10.330441 | 0.9904 | 0.7840 |
3.9665 | 25100 | 0.0974 | - | - | - | - |
3.9823 | 25200 | 0.097 | - | - | - | - |
3.9981 | 25300 | 0.0978 | - | - | - | - |
4.0139 | 25400 | 0.0969 | - | - | - | - |
4.0297 | 25500 | 0.0966 | - | - | - | - |
4.0455 | 25600 | 0.0965 | - | - | - | - |
4.0613 | 25700 | 0.0974 | - | - | - | - |
4.0771 | 25800 | 0.0966 | - | - | - | - |
4.0929 | 25900 | 0.0964 | - | - | - | - |
4.1087 | 26000 | 0.0961 | - | - | - | - |
4.1245 | 26100 | 0.0958 | - | - | - | - |
4.1403 | 26200 | 0.0964 | - | - | - | - |
4.1561 | 26300 | 0.097 | - | - | - | - |
4.1719 | 26400 | 0.0967 | - | - | - | - |
4.1877 | 26500 | 0.0968 | - | - | - | - |
4.2035 | 26600 | 0.0965 | - | - | - | - |
4.2193 | 26700 | 0.0956 | - | - | - | - |
4.2351 | 26800 | 0.0963 | - | - | - | - |
4.2509 | 26900 | 0.0958 | - | - | - | - |
4.2668 | 27000 | 0.0969 | - | - | - | - |
4.2826 | 27100 | 0.0951 | - | - | - | - |
4.2984 | 27200 | 0.0958 | - | - | - | - |
4.3142 | 27300 | 0.0956 | - | - | - | - |
4.3300 | 27400 | 0.0965 | - | - | - | - |
4.3458 | 27500 | 0.0952 | - | - | - | - |
4.3616 | 27600 | 0.0956 | - | - | - | - |
4.3774 | 27700 | 0.0956 | - | - | - | - |
4.3932 | 27800 | 0.0966 | - | - | - | - |
4.4090 | 27900 | 0.0972 | - | - | - | - |
4.4248 | 28000 | 0.0954 | - | - | - | - |
4.4406 | 28100 | 0.0961 | - | - | - | - |
4.4564 | 28200 | 0.0963 | - | - | - | - |
4.4722 | 28300 | 0.0958 | - | - | - | - |
4.4880 | 28400 | 0.0961 | - | - | - | - |
4.5038 | 28500 | 0.0961 | - | - | - | - |
4.5196 | 28600 | 0.0956 | - | - | - | - |
4.5354 | 28700 | 0.0955 | - | - | - | - |
4.5512 | 28800 | 0.0957 | - | - | - | - |
4.5670 | 28900 | 0.0953 | - | - | - | - |
4.5828 | 29000 | 0.0952 | - | - | - | - |
4.5986 | 29100 | 0.0964 | - | - | - | - |
4.6144 | 29200 | 0.0955 | - | - | - | - |
4.6302 | 29300 | 0.0948 | - | - | - | - |
4.6460 | 29400 | 0.0946 | - | - | - | - |
4.6618 | 29500 | 0.0953 | - | - | - | - |
4.6776 | 29600 | 0.0954 | - | - | - | - |
4.6934 | 29700 | 0.0956 | - | - | - | - |
4.7092 | 29800 | 0.0958 | - | - | - | - |
4.7250 | 29900 | 0.0956 | - | - | - | - |
4.7408 | 30000 | 0.0962 | 0.0900 | -10.183619 | 0.9894 | 0.7903 |
4.7566 | 30100 | 0.0953 | - | - | - | - |
4.7724 | 30200 | 0.0959 | - | - | - | - |
4.7882 | 30300 | 0.0949 | - | - | - | - |
4.8040 | 30400 | 0.0958 | - | - | - | - |
4.8198 | 30500 | 0.0952 | - | - | - | - |
4.8357 | 30600 | 0.0952 | - | - | - | - |
4.8515 | 30700 | 0.095 | - | - | - | - |
4.8673 | 30800 | 0.0949 | - | - | - | - |
4.8831 | 30900 | 0.0949 | - | - | - | - |
4.8989 | 31000 | 0.0953 | - | - | - | - |
4.9147 | 31100 | 0.0955 | - | - | - | - |
4.9305 | 31200 | 0.0964 | - | - | - | - |
4.9463 | 31300 | 0.0955 | - | - | - | - |
4.9621 | 31400 | 0.0955 | - | - | - | - |
4.9779 | 31500 | 0.0954 | - | - | - | - |
4.9937 | 31600 | 0.0959 | - | - | - | - |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.46.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.20.3
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",
}
MSELoss
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
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Base model
FacebookAI/xlm-roberta-baseDataset used to train vallabh001/xlm-roberta-base-multilingual-en-es
Evaluation results
- Negative Mse on en esself-reported-10.184
- Src2Trg Accuracy on en esself-reported0.988
- Trg2Src Accuracy on en esself-reported0.991
- Mean Accuracy on en esself-reported0.989
- Pearson Cosine on sts17 es en testself-reported0.767
- Spearman Cosine on sts17 es en testself-reported0.790