MNLP_M3_document_encoder / README.md

DoDucAnh

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:594028
  - loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
  - source_sentence: "While driving her company vehicle near a pedestrian mall, a woman came upon the scene of a three-car accident. She was so busy gawking at the damaged vehicles that she failed to see one of the victims lying on the road in front of her car. She hit and ran over the victim, who survived and sued the woman's company. The victim offers the testimony of a witness to the incident. Referring to the woman, the witness stated, \"The driver of that car ran over the victim as he was lying on the ground awaiting an ambulance, and said  \x80\x98It is all my fault, I should have been paying more attention to my driving. \" Assume for this question that the woman is available to testify. The trial judge should rule that the testimony is\nA. admissible as a declaration against interest.\nB. admissible as a present sense impression.\nC. admissible as an admission.\nD. inadmissible as hearsay not within any recognized exception."
    sentences:
      - >-
        A present sense impression is a statement describing or explaining an
        event or condition made while the declarant was perceiving the event or
        condition, or immediately thereafter. This is an exception to the
        hearsay rule.
      - >-
        Corporate managers are professionals within a business environment who
        handle various aspects of management, including planning, organizing,
        leading, and controlling resources. Their roles often draw from
        established management theories, such as those by Henri Fayol, which
        emphasize functions like forecasting, commanding, and coordinating to
        support organizational success.
      - >-
        Ο ποιοτικός έλεγχος είναι μια διαδικασία που εφαρμόζεται στη βιομηχανία
        και σε άλλους τομείς για να διασφαλιστεί ότι τα προϊόντα ή οι υπηρεσίες
        πληρούν συγκεκριμένες προδιαγραφές και πρότυπα ποιότητας.
  - source_sentence: |-
      ‘বিপরীত বৈষম্য’-এর নীতিটি প্রয়োগ করা হয়-
      A. পিছিয়ে পড়া জনগােষ্ঠীর ক্ষেত্রে
      B. . নারীদের ক্ষেত্রে
      C. প্রতিবন্ধীদের ক্ষেত্রে
      D. সংখ্যালঘুদের ক্ষেত্রে
    sentences:
      - >-
        বিপরীত বৈষম্য সাধারণত পিছিয়ে পড়া জনগোষ্ঠী বা সংখ্যালঘুদের মতো গোষ্ঠীর
        ক্ষেত্রে প্রয়োগ করা হয় যারা ঐতিহাসিকভাবে বৈষম্যের শিকার হয়েছেন।
      - >-
        Hummingbirds reproduce by laying eggs, usually in small nests that they
        build on branches. The female is responsible for incubating the eggs and
        caring for the young, which necessitates energy management to ensure
        survival and growth.
      - >-
        In the Mughal Empire, zamindars were initially indigenous local chiefs
        of towns and villages in rural areas. Later, they became landholders who
        could collect taxes from peasants and tenants, transmitting a tenth or
        eleventh of their produce to the imperial treasury. In contrast to the
        jagirdars, who were given land grants as part of their service to the
        Mughal government, the zamindar tenure was hereditary. The zamindars
        performed the functions of the ancient rajas (kings) or chieftains. They
        were landowners who were expected to pay a fixed tribute to the Mughal
        emperor.
  - source_sentence: >-
      In a global context, many companies have significant ______ power due to
      their ability to threaten governments, in the face of ________ with
      relocation to other territories, which Beck (1998) describes as 'corporate
      power of _______.

      A. Economic, Commercial competition, Social sanction

      B. Political, Undesirable regulation, Transnational withdrawal

      C. Social, Commercial competition, Social sanction

      D. Social, Undesirable regulation, Transnational withdrawal
    sentences:
      - >-
        L'anémie est une condition caractérisée par une diminution du nombre de
        globules rouges ou de la quantité d'hémoglobine dans le sang, entraînant
        une réduction du transport de l'oxygène.
      - >-
        Another critical method for evaluating internal controls is to focus on
        risk identification and the specific potential losses associated with
        those risks. Organizations often start with a thorough risk analysis to
        understand vulnerabilities, which can then inform the development or
        enhancement of control activities intended to mitigate those risks.
      - >-
        The concept of 'transnational withdrawal' refers to the phenomenon where
        companies threaten to relocate their operations to countries with more
        favorable conditions. This can include lighter regulations, lower taxes,
        or more lenient labor standards. The threat of relocation can compel
        governments to modify their policies or regulations to keep corporations
        within their jurisdictions, thereby illustrating the leverage that
        global companies hold.
  - source_sentence: >-
      Can armed violence perpetrated by non-State actors ever amount to an armed
      attack under Article 51 UN Charter?

      A. The conduct of non-State actors can never amount to an armed attack

      B. The Caroline case serves as precedent that non-State actors can under
      particular circumstances commit an armed attack

      C. There is no precedent in international law for the proposition that
      non-State actors can commit an armed attack

      D. Non-State can both commit an armed attack and possess a right of
      self-defence under international law
    sentences:
      - >-
        In international law, the concept of an armed attack typically refers to
        the use of force by one state against another, which is significant
        under the UN Charter as it may trigger the right of self-defense. This
        term is often discussed in the context of customary international law
        and the interpretations by bodies like the International Court of
        Justice.
      - >-
        2. **Force and Motion**: According to Newton's second law, the
        acceleration of an object is directly proportional to the net force
        acting on it and inversely proportional to its mass (F = ma). If an
        object can accelerate in response to a force, this indicates that the
        force applied contributes to the net work done on the object, thereby
        altering its kinetic energy.
      - 委託に伴って個人データを提供する場合、委託先は「第三者」に該当しないとみなされることがあります。この場合、原則として本人の同意は不要です。
  - source_sentence: >-
      A builder had a contract to build a swimming pool for a residential
      customer. That customer's next door neighbor went to the builder and paid
      him extra to break the contract with the customer and instead to build a
      swimming pool on the neighbor's premises. The builder commenced building a
      swimming pool for the neighbor and breached his contract with the original
      customer. The original customer sued his neighbor in a tort claim for
      damages. Does the original customer have a valid claim against his
      neighbor?

      A. Yes, the neighbor committed the tort of interference with contract
      relations by intentionally interfering with an existing contract.

      B. No, people cannot be held in slavery

      C. they have the right to contract with whomever they please.

      D. No, the only remedy for the original customer is to sue the builder for
      breach of contract.

      E. Yes, the neighbor committed the tort of interference with prospective
      advantage.
    sentences:
      - >-
        Ներքին գործերի նախարար - Պաշտոն, որը պատասխանատու է երկրի ներքին
        անվտանգության, հասարակական կարգի և օրենքի պահպանման համար։
      - >-
        A tort is a civil wrong that causes harm or loss to another person,
        resulting in legal liability for the person who commits the tort. Tort
        law allows individuals to seek compensation for injuries or damages
        caused by the wrongful acts of others, distinct from breaches of
        contract.
      - >-
        Substance use, such as alcohol and tobacco, during pregnancy can lead to
        various complications including low birth weight, developmental issues,
        and increased risk of infections, highlighting the importance of
        cessation and support for affected mothers.
datasets:
  - DoDucAnh/MNLP_M3_rag_dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on intfloat/multilingual-e5-large-instruct

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the mnlp_m3_rag_dataset dataset. 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: intfloat/multilingual-e5-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- mnlp_m3_rag_dataset

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': 1024, '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})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    "A builder had a contract to build a swimming pool for a residential customer. That customer's next door neighbor went to the builder and paid him extra to break the contract with the customer and instead to build a swimming pool on the neighbor's premises. The builder commenced building a swimming pool for the neighbor and breached his contract with the original customer. The original customer sued his neighbor in a tort claim for damages. Does the original customer have a valid claim against his neighbor?\nA. Yes, the neighbor committed the tort of interference with contract relations by intentionally interfering with an existing contract.\nB. No, people cannot be held in slavery\nC. they have the right to contract with whomever they please.\nD. No, the only remedy for the original customer is to sue the builder for breach of contract.\nE. Yes, the neighbor committed the tort of interference with prospective advantage.",
    'A tort is a civil wrong that causes harm or loss to another person, resulting in legal liability for the person who commits the tort. Tort law allows individuals to seek compensation for injuries or damages caused by the wrongful acts of others, distinct from breaches of contract.',
    'Substance use, such as alcohol and tobacco, during pregnancy can lead to various complications including low birth weight, developmental issues, and increased risk of infections, highlighting the importance of cessation and support for affected mothers.',
]
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]

Training Details

Training Dataset

mnlp_m3_rag_dataset

Dataset: mnlp_m3_rag_dataset at e16d937
Size: 594,028 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 21 tokens
mean: 359.4 tokens
max: 512 tokens

min: 4 tokens
mean: 56.63 tokens
max: 433 tokens

	anchor	positive
type	string	string
details	min: 21 tokens mean: 359.4 tokens max: 512 tokens	min: 4 tokens mean: 56.63 tokens max: 433 tokens

Samples:

anchor	positive
Little Lopsy fluttered into our home and our hearts one Saturday morning this summer. My husband went out to do something, and when he opened the door there was a great flutter on the ground and something came into the living room. It was clear that whatever it was was hurt. I was in a bit of a shock and didn't know what to do next. Fortunately it calmed down and tried to hide itself in a corner. I realized it was a sparrow chick . There are a few sparrow nests under the roof of our apartment, and this little fellow must have fallen out and hurt itself. It was also very young, and obviously far from ready to leave the safety of the nest. I ran to the place and found a box. Having read somewhere that one shouldn't touch a baby bird with one's hands, I picked the chick up with a hand towel and put it in the box. I placed the box outside the front door in the hope that the parents would try to feed it. They never came near it and I brought it inside. I placed the box on a table and it sl...	`Having read somewhere that one shouldn't touch a baby bird with one's hands, I picked the chick up with a hand towel and put it in the box.`
`A thermal conductor is made of A. types of rubber B. types of wire C. electrodes D. that which conducts`	`A thermal conductor is a material that allows heat to flow through it easily. Common examples of thermal conductors include metals such as copper and aluminum, known for their high thermal conductivity due to their free-flowing electrons. Heat transfer occurs via conduction when heat energy moves from the hotter part of a conductor to the cooler part, often described by Fourier's Law of heat conduction.`
`A good example of increased demand may equal increased production is A. soldiers eat beans, so beans are planted when there is war B. dogs eat kibble, so stores sell it C. cats eat mice, so mice are afraid of cats D. people have babies, so baby clothes are made`	`Supply is the total amount of a specific good or service that is available to consumers. Supply can relate to the amount available at a specific price or the amount available across a range of prices if displayed on a graph.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

mnlp_m3_rag_dataset

Dataset: mnlp_m3_rag_dataset at e16d937
Size: 5,920 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 22 tokens
mean: 98.74 tokens
max: 512 tokens

min: 9 tokens
mean: 59.88 tokens
max: 501 tokens

	anchor	positive
type	string	string
details	min: 22 tokens mean: 98.74 tokens max: 512 tokens	min: 9 tokens mean: 59.88 tokens max: 501 tokens

Samples:

anchor	positive
`ക്രൂരകോഷ്ഠം ഉള്ള ഒരാളിൽ കോപിച്ചിരിക്കുന്ന ദോഷം താഴെപ്പറയുന്നവയിൽ ഏതാണ്? A. കഫം B. പിത്തം C. വാതം D. രക്തം`	`ഓരോ ദോഷത്തിനും അതിന്റേതായ സ്വഭാവങ്ങളും ശരീരത്തിൽ അത് ഉണ്ടാക്കുന്ന ഫലങ്ങളും ഉണ്ട്.`
`Melyik tényező nem befolyásolja a fagylalt keresleti függvényét? A. A fagylalt árának változása. B. Mindegyik tényező befolyásolja. C. A jégkrém árának változása. D. A fagylalttölcsér árának változása.`	`A keresleti függvény negatív meredekségű, ami azt jelenti, hogy az ár növekedésével a keresett mennyiség csökken (csökkenő kereslet törvénye).`
In contrast to _______, _______ aim to reward favourable behaviour by companies. The success of such campaigns have been heightened through the use of ___________, which allow campaigns to facilitate the company in achieving _________ . A. Boycotts, Buyalls, Blockchain technology, Increased Sales B. Buycotts, Boycotts, Digital technology, Decreased Sales C. Boycotts, Buycotts, Digital technology, Decreased Sales D. Buycotts, Boycotts, Blockchain technology, Charitable donations E. Boycotts, Buyalls, Blockchain technology, Charitable donations F. Boycotts, Buycotts, Digital technology, Increased Sales G. Buycotts, Boycotts, Digital technology, Increased Sales H. Boycotts, Buycotts, Physical technology, Increased Sales I. Buycotts, Buyalls, Blockchain technology, Charitable donations J. Boycotts, Buycotts, Blockchain technology, Decreased Sales	`Consumer Activism: This term refers to the actions taken by consumers to promote social, political, or environmental causes. These actions can include boycotting certain companies or buycotting others, influencing market dynamics based on ethical considerations. The effectiveness of consumer activism can vary but has gained prominence in recent years with increased visibility through social media.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 2
learning_rate: 2e-05
warmup_steps: 5569
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
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: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 5569
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: 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: True
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
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: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.15	2785	0.2684	0.0411
0.3001	5570	0.1112	0.0541
0.4501	8355	0.1153	0.0633
0.6001	11140	0.1045	0.0582
0.7501	13925	0.0943	0.0606
0.9002	16710	0.0883	0.0563
1.0502	19495	0.0744	0.0505
1.2002	22280	0.0592	0.0523
1.3502	25065	0.059	0.0516
1.5002	27850	0.0544	0.0617
1.6503	30635	0.0521	0.0549
1.8003	33420	0.0502	0.0589
1.9503	36205	0.0449	0.0550
2.1003	38990	0.0369	0.0619
2.2503	41775	0.0331	0.0604
2.4004	44560	0.0308	0.0566
2.5504	47345	0.0294	0.0533
2.7004	50130	0.0286	0.0531
2.8504	52915	0.0266	0.0537

The bold row denotes the saved checkpoint. The training took 6h52m on a RTX5090

Framework Versions

Python: 3.12.3
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.7.0+cu128
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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}
}