---
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](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [mnlp_m3_rag_dataset](https://huggingface.co/datasets/DoDucAnh/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](https://huggingface.co/intfloat/multilingual-e5-large-instruct)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [mnlp_m3_rag_dataset](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset)
### 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: 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:
```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("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](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset) at [e16d937](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset/tree/e16d937f0bb9981bb081e8c16a3eda5b3fbbc68a)
* 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
|
* 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### mnlp_m3_rag_dataset
* Dataset: [mnlp_m3_rag_dataset](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset) at [e16d937](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset/tree/e16d937f0bb9981bb081e8c16a3eda5b3fbbc68a)
* 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
|
* 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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