|
|
--- |
|
|
tags: |
|
|
- sentence-transformers |
|
|
- sentence-similarity |
|
|
- feature-extraction |
|
|
- generated_from_trainer |
|
|
- dataset_size:46716 |
|
|
- loss:MultipleNegativesRankingLoss |
|
|
base_model: sentence-transformers/all-MiniLM-L6-v2 |
|
|
widget: |
|
|
- source_sentence: Structurally, diplomonads have two equal-sized what and multiple |
|
|
flagella? |
|
|
sentences: |
|
|
- deciding when to buy or sell a stock is not an easy task because the market is |
|
|
hard to predict, being influenced by political and economic factors. thus, methodologies |
|
|
based on computational intelligence have been applied to this challenging problem. |
|
|
in this work, every day the stocks are ranked by technique for order preference |
|
|
by similarity to ideal solution ( topsis ) using technical analysis criteria, |
|
|
and the most suitable stock is selected for purchase. even so, it may occur that |
|
|
the market is not favorable to purchase on certain days, or even, the topsis make |
|
|
an incorrect selection. to improve the selection, another method should be used. |
|
|
so, a hybrid |
|
|
- 'we present the analysis of the brightest flare that was recorded in the \ emph |
|
|
{ insight } - hmxt data set, in a broad energy range ( 2 $ - $ 200 kev ) from |
|
|
the microquasar grs ~ 1915 + 105 during an unusual low - luminosity state. this |
|
|
flare was detected by \ emph { insight } - hxmt among a series of flares during |
|
|
2 june 2019 utc 16 : 37 : 06 to 20 : 11 : 36, with a 2 - 200 kev luminosity of |
|
|
3. 4 $ - $ 7. 27 $ \ times10 ^ { 38 } $ er' |
|
|
- nuclei |
|
|
- source_sentence: What instruments used in guidance systems to indicate directions |
|
|
in space must have an angular momentum that does not change in direction? |
|
|
sentences: |
|
|
- magnetic and transport properties of near - stoichiometric metastable fexmnygaz |
|
|
alloys ( 46 < x < 52, 17 < y25, 26 < z < 30 ) with face - centered cubic ( fcc |
|
|
), body - centered cubic ( bcc ), and two - phase ( fcc + bcc ) structures are |
|
|
investigated. the experimental results are analyzed in terms of first - principles |
|
|
calculations of stoichiometric fe2mnga alloy with the l21, l12, and the tetragonally |
|
|
distorted l21 structural orderings. it is shown that the pure bcc and fcc phases |
|
|
have distinct magnetic |
|
|
- k nearest neighbor ( knn ) joins are used in scientific domains for data analysis, |
|
|
and are building blocks of several well - known algorithms. knn - joins find the |
|
|
knn of all points in a dataset. this paper focuses on a hybrid cpu / gpu approach |
|
|
for low - dimensional knn - joins, where the gpu may not yield substantial performance |
|
|
gains over parallel cpu algorithms. we utilize a work queue that prioritizes computing |
|
|
data points in high density regions on the gpu, and low density regions on the |
|
|
cpu, thereby taking advantage of each architecture ' s relative strengths. our |
|
|
approach, hybridknn - join, |
|
|
- the fact that these states are effectively decoupled from propagating photons. |
|
|
we prove that scattering of a parity - invariant single photon on a qubit pair, |
|
|
combined with a properly engineered time variation of the qubit detuning, is not |
|
|
only feasible, but also more effective than strategies based on the relaxation |
|
|
of the excited states of the qubits. the use of tensor network methods to simulate |
|
|
the proposed scheme enables to include photon delays in collision models, thus |
|
|
opening the possibility to follow the time evolution of the full quantum system, |
|
|
including qubits and field, and to efficiently implement and characterize the |
|
|
dynamics in non |
|
|
- source_sentence: If pollination and fertilization occur, a diploid zygote forms |
|
|
within an ovule, located where |
|
|
sentences: |
|
|
- while cosmic rays $ ( e \ gtrsim 1 \, \ mathrm { gev } ) $ are well coupled to |
|
|
a galaxy ' s interstellar medium ( ism ) at scales of $ l > 100 \, \ mathrm { |
|
|
pc } $, adjusting stratification and driving outflows, their impact on small scales |
|
|
is less clear. based on calculations of the cosmic ray diffusion coefficient from |
|
|
observations of the grammage in the milky way, cosmic rays have little time to |
|
|
dynamically impact the ism on those small scales. using numerical simulations, |
|
|
we explore how more complex cosmic ray transport could allow cosmic rays to couple |
|
|
- centripetal force |
|
|
- derived simple analytical expressions for the maximum growth rate, corresponding |
|
|
to the most unstable mode of the system. these expressions provide the explicit |
|
|
dependence of the growth rate on the various equilibrium parameters. for small |
|
|
angles the growth time is linearly proportional to the shear angle, and in this |
|
|
regime the single interface problem and the slab problem tend to the same result. |
|
|
on the contrary, in the limit of large angles and for the interface problem the |
|
|
growth time is essentially independent of the shear angle. in this regime we have |
|
|
also been able to calculate an approximate expression for the growth time for |
|
|
the slab configuration. magnetic shear can have a strong effect on the growth |
|
|
rates |
|
|
- source_sentence: When the hydrogen is nearly used up, the star can fuse which element |
|
|
into heavier elements? |
|
|
sentences: |
|
|
- the 50 ~ kton iron calorimeter ( ical ) detector at the underground india based |
|
|
neutrino observatory ( ino ) will make measurements on atmospheric neutrinos. |
|
|
muons produced in charged current ( cc ) interactions of muon neutrinos with the |
|
|
iron are tracked spatially and temporally through the signals that they produce |
|
|
in the resistive plate chambers ~ ( rpcs ) that are interleaved with iron layers. |
|
|
since the rpcs will be operated in the avalanche mode the signal rise - time is |
|
|
$ \ sim ~ 1 ~ \ rm { nsec } $ resulting in a fast time response |
|
|
- magnesium in air |
|
|
- pbhs. after discussing pbh formation as well as several inflation models leading |
|
|
to pbh production, we summarize various existing and future observational constraints. |
|
|
we then present topics on formation of pbh binaries, gravitational waves from |
|
|
pbh binaries, various observational tests of pbhs by using gravitational waves. |
|
|
- source_sentence: How many different main types of diabetes are there? |
|
|
sentences: |
|
|
- skin |
|
|
- two |
|
|
- a connection between relativistic quantum mechanics in the foldy - wouthuysen |
|
|
representation and the paraxial equations is established for a dirac particle |
|
|
in external fields. the paraxial form of the landau eigenfunction for a relativistic |
|
|
electron in a uniform magnetic field is determined. the obtained wave function |
|
|
contains the gouy phase and significantly approaches to the paraxial wave function |
|
|
for a free electron. |
|
|
pipeline_tag: sentence-similarity |
|
|
library_name: sentence-transformers |
|
|
metrics: |
|
|
- cosine_accuracy@1 |
|
|
- cosine_accuracy@3 |
|
|
- cosine_accuracy@5 |
|
|
- cosine_accuracy@10 |
|
|
- cosine_precision@1 |
|
|
- cosine_precision@3 |
|
|
- cosine_precision@5 |
|
|
- cosine_precision@10 |
|
|
- cosine_recall@1 |
|
|
- cosine_recall@3 |
|
|
- cosine_recall@5 |
|
|
- cosine_recall@10 |
|
|
- cosine_ndcg@10 |
|
|
- cosine_mrr@10 |
|
|
- cosine_map@100 |
|
|
model-index: |
|
|
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
|
|
results: |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
|
|
name: sciq eval |
|
|
type: sciq-eval |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.084 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.192 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.26 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.367 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.084 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.064 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.052 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.0367 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.084 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.192 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.26 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.367 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.20773622543165599 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.1588857142857143 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.17411230071844377 |
|
|
name: Cosine Map@100 |
|
|
--- |
|
|
|
|
|
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
|
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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. |
|
|
|
|
|
## Model Details |
|
|
|
|
|
### Model Description |
|
|
- **Model Type:** Sentence Transformer |
|
|
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
|
|
- **Maximum Sequence Length:** 256 tokens |
|
|
- **Output Dimensionality:** 384 dimensions |
|
|
- **Similarity Function:** Cosine Similarity |
|
|
<!-- - **Training Dataset:** Unknown --> |
|
|
<!-- - **Language:** Unknown --> |
|
|
<!-- - **License:** Unknown --> |
|
|
|
|
|
### 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': 256, 'do_lower_case': False}) with Transformer model: BertModel |
|
|
(1): Pooling({'word_embedding_dimension': 384, '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 = [ |
|
|
'How many different main types of diabetes are there?', |
|
|
'two', |
|
|
'a connection between relativistic quantum mechanics in the foldy - wouthuysen representation and the paraxial equations is established for a dirac particle in external fields. the paraxial form of the landau eigenfunction for a relativistic electron in a uniform magnetic field is determined. the obtained wave function contains the gouy phase and significantly approaches to the paraxial wave function for a free electron.', |
|
|
] |
|
|
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] |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
|
|
|
|
|
You can finetune this model on your own dataset. |
|
|
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Out-of-Scope Use |
|
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
|
--> |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### Metrics |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `sciq-eval` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.084 | |
|
|
| cosine_accuracy@3 | 0.192 | |
|
|
| cosine_accuracy@5 | 0.26 | |
|
|
| cosine_accuracy@10 | 0.367 | |
|
|
| cosine_precision@1 | 0.084 | |
|
|
| cosine_precision@3 | 0.064 | |
|
|
| cosine_precision@5 | 0.052 | |
|
|
| cosine_precision@10 | 0.0367 | |
|
|
| cosine_recall@1 | 0.084 | |
|
|
| cosine_recall@3 | 0.192 | |
|
|
| cosine_recall@5 | 0.26 | |
|
|
| cosine_recall@10 | 0.367 | |
|
|
| **cosine_ndcg@10** | **0.2077** | |
|
|
| cosine_mrr@10 | 0.1589 | |
|
|
| cosine_map@100 | 0.1741 | |
|
|
|
|
|
<!-- |
|
|
## Bias, Risks and Limitations |
|
|
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Recommendations |
|
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
|
--> |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Dataset |
|
|
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 46,716 training samples |
|
|
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | sentence_0 | sentence_1 | label | |
|
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
|
| type | string | string | float | |
|
|
| details | <ul><li>min: 7 tokens</li><li>mean: 17.62 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 92.89 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 1.0</li></ul> | |
|
|
* Samples: |
|
|
| sentence_0 | sentence_1 | label | |
|
|
|:--------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
|
|
| <code>Unlike plants, animal species rely almost exclusively on what type of reproduction?</code> | <code>0. 4, \ sim 4, and \ sim 300 \ mum are stronger than 10 ^ 5, 10 ^ 8, and 10 ^ 4 times those of the local interstellar radiation field ( isrf ). below these values, the chemical pumping is the dominant source of excitation of the j > 1 levels, even at high kinetic temperatures ( \ sim 1000 k ). the far - infrared emission lines of ch + observed in the orion bar and the ngc 7027 pdrs are consistent with the predictions of our excitation model assuming an incident far - ultraviolet ( fuv ) radiation field of \ sim 3 \ times 10</code> | <code>0.0</code> | |
|
|
| <code>What type of energy occurs by splitting the nuclei of radioactive uranium?</code> | <code>we study the potential of future electron - ion collider ( eic ) data to probe four - fermion operators in the standard model effective field theory ( smeft ). the ability to perform measurements with both polarized electron and proton beams at the eic provides a powerful tool that can disentangle the effects from different smeft operators. we compare the potential constraints from an eic with those obtained from drell - yan data at the large hadron collider. we show that eic data plays an important complementary role since it probes combinations of wilson coefficients not accessible through available drell - yan measurements.</code> | <code>0.0</code> | |
|
|
| <code>What element, which often forms polymers, has a unique ability to form covalent bonds with many other atoms?</code> | <code>some divergent series $ f $. the convergence sets on $ \ gamma : = \ { [ 1 : z : \ psi ( z ) ] : z \ in \ mathbb { c } \ } \ subset \ mathbb { c } ^ 2 \ subset \ mathbb { p } ^ 2 $, where $ \ psi $ is a transcendental entire holomorphic function, are also studied and we obtain that a subset on $ \ gamma $ is a convergence set in $ \ mathbb { p } ^ 2 $ if and only if it is a countable union of compact projectively convex sets, and</code> | <code>0.0</code> | |
|
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](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`: 32 |
|
|
- `per_device_eval_batch_size`: 32 |
|
|
- `num_train_epochs`: 1 |
|
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: steps |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 32 |
|
|
- `per_device_eval_batch_size`: 32 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 1 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_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 |
|
|
- `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`: False |
|
|
- `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} |
|
|
- `tp_size`: 0 |
|
|
- `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`: round_robin |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | sciq-eval_cosine_ndcg@10 | |
|
|
|:------:|:----:|:-------------:|:------------------------:| |
|
|
| 0.0685 | 100 | - | 0.1200 | |
|
|
| 0.1370 | 200 | - | 0.1562 | |
|
|
| 0.2055 | 300 | - | 0.1780 | |
|
|
| 0.2740 | 400 | - | 0.1811 | |
|
|
| 0.3425 | 500 | 3.1705 | 0.1909 | |
|
|
| 0.4110 | 600 | - | 0.1904 | |
|
|
| 0.4795 | 700 | - | 0.1955 | |
|
|
| 0.5479 | 800 | - | 0.2031 | |
|
|
| 0.6164 | 900 | - | 0.2014 | |
|
|
| 0.6849 | 1000 | 2.9054 | 0.2002 | |
|
|
| 0.7534 | 1100 | - | 0.2058 | |
|
|
| 0.8219 | 1200 | - | 0.2083 | |
|
|
| 0.8904 | 1300 | - | 0.2084 | |
|
|
| 0.9589 | 1400 | - | 0.2076 | |
|
|
| 1.0 | 1460 | - | 0.2077 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.8 |
|
|
- Sentence Transformers: 3.4.1 |
|
|
- Transformers: 4.51.3 |
|
|
- PyTorch: 2.5.1+cu124 |
|
|
- Accelerate: 1.3.0 |
|
|
- Datasets: 3.2.0 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@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 |
|
|
```bibtex |
|
|
@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} |
|
|
} |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
## Glossary |
|
|
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Model Card Authors |
|
|
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Model Card Contact |
|
|
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
|
--> |
