metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:100K<n<1M
  - loss:MultipleNegativesRankingLoss
base_model: FacebookAI/xlm-roberta-base
widget:
  - source_sentence: who did ezra play for in the nfl
    sentences:
      - how many all nba first teams does kobe have
      - who does the voice of the little mermaid
      - dont come around here no more video director
  - source_sentence: who led the elves at helm s deep
    sentences:
      - who was the captain of the flying dutchman
      - what are the 2 seasons in the philippines
      - when can you get a tattoo in georgia
  - source_sentence: who plays red on once upon a time
    sentences:
      - who plays the new receptionist on the office
      - who wrote the magic school bus theme song
      - when did south africa declare war on germany
  - source_sentence: who plays the dark elf in thor 2
    sentences:
      - who plays mantis in guardian of the galaxy 2
      - where in los angeles do the chargers play
      - when did alaska become part of the us
  - source_sentence: who plays oz in the wizard of oz
    sentences:
      - where did the wizard of oz come from
      - when did brazil win the soccer world cup
      - when did the ar 15 first go on sale
pipeline_tag: sentence-similarity

SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base. 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: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

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: 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("Stern5497/nir-2024-xlm-roberta-base")
# Run inference
sentences = [
    'who plays oz in the wizard of oz',
    'where did the wizard of oz come from',
    'when did brazil win the soccer world cup',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 164,848 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 10 tokens mean: 13.41 tokens max: 27 tokens	min: 136 tokens mean: 164.07 tokens max: 239 tokens	min: 133 tokens mean: 165.13 tokens max: 256 tokens

Samples:

sentence_0	sentence_1	sentence_2
`who wrote treat you better by shawn mendes`	{'title': '', 'text': 'Treat You Better "Treat You Better" is a song recorded by Canadian singer and songwriter Shawn Mendes. It was co-written by Mendes with Teddy Geiger, and Scott Harris. It was released on June 3, 2016 through Island Records as the lead single from his second studio album, "Illuminate" (2016). The music video was released on July 12, 2016 and features a storyline about an abusive relationship. The song peaked at number six on the US "Billboard" Hot 100, making it Mendes' second top 10 single. In Canada, the song has peaked at number seven on the Canadian Hot 100. The'}	{'title': '', 'text': 'Scott Harris (songwriter) Scott Harris Friedman is an American multi-platinum, Grammy nominated songwriter, producer, and musician best known for his work with Shawn Mendes and co-writing Grammy winning song, "Don't Let Me Down" by The Chainsmokers featuring Daya, which reached #1 on the US Mainstream Top 40 chart in 2016. Harris has most recently written 13 songs on the self-titled third album Shawn Mendes (album), which debuted at #1 on the Billboard 200 chart, in addition to 10 songs on Shawn Mendes' sophomore album "Illuminate" including the lead single "Treat You Better" which reached the top 3 at the US'}
`where is the tanami desert located in australia`	{'title': '', 'text': 'zone. Tanami Desert The Tanami Desert is a desert in northern Australia situated in the Northern Territory and Western Australia. It has a rocky terrain with small hills. The Tanami was the Northern Territory's final frontier and was not fully explored by Australians of European descent until well into the twentieth century. It is traversed by the Tanami Track. The name "Tanami" is thought to be a corruption of the Walpiri name for the area, "Chanamee", meaning "never die". This referred to certain rock holes in the desert which were said never to run dry. Under the name "Tanami", the'}	{'title': '', 'text': '("glomerata") is from the Latin "glomeratus", meaning "heaped" or "form into a ball". Desert tea-tree occurs in the arid parts of Australia including the far north west of New South Wales, South Australia including the Flinders Ranges, the Northern Territory and Western Australia. In the latter state it has been recorded from the Carnarvon, Central Kimberley, Central Ranges, Dampierland, Gascoyne, Gibson Desert, Great Sandy Desert, Great Victoria Desert, Little Sandy Desert, Murchison, Ord Victoria Plain, Pilbara and Tanami biogeographic areas. It grows in red sand, clay and sandy loam in rocky river beds, shallow depressions and sandy flats. "Melaleuca globifera"'}
`who won the us open men s and women s singles in 2017`	{'title': '', 'text': "that ended his season, while Kerber lost in the first round to Naomi Osaka. The men's singles tournament concluded with Rafael Nadal defeating Kevin Anderson in the final, while the women's singles tournament concluded with Sloane Stephens defeating Madison Keys in the final. The 2017 US Open was the 137th edition of the tournament and took place at the USTA Billie Jean King National Tennis Center in Flushing Meadows–Corona Park of Queens in New York City, New York, United States. The tournament was held on 14 DecoTurf hard courts. The tournament was an event run by the International Tennis Federation"}	{'title': '', 'text': "2017 US Open – Women's Singles Angelique Kerber was the defending champion, but was defeated in the first round by Naomi Osaka. Kerber became the second US Open defending champion to lose in the first round after Svetlana Kuznetsova in 2005. Sloane Stephens won her first Grand Slam title, defeating Madison Keys in the final, 6–3, 6–0. It was the first all-American women's final at the US Open since 2002, and the second time in three years that the final featured two first-time Grand Slam singles finalists from the same country. Stephens became the second unseeded woman in the Open"}

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
fp16: True
batch_sampler: no_duplicates
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1
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
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, '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: 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_sampler: no_duplicates
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.0485	500	1.6163
0.0971	1000	0.8086
0.1456	1500	0.6766
0.1941	2000	0.6124
0.2426	2500	0.5374
0.2912	3000	0.5115
0.3397	3500	0.4823
0.3882	4000	0.4268
0.4368	4500	0.422
0.4853	5000	0.4014
0.5338	5500	0.3765
0.5824	6000	0.3689
0.6309	6500	0.3551
0.6794	7000	0.3359
0.7279	7500	0.326
0.7765	8000	0.3158
0.8250	8500	0.2945
0.8735	9000	0.2836
0.9221	9500	0.3043
0.9706	10000	0.2761
1.0	10303	-

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.40.2
PyTorch: 2.3.0+cu118
Accelerate: 0.29.3
Datasets: 2.19.0
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}
}