---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:82169
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: can beef help reduce emissions
sentences:
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals These studies each shed light on the quantitative effects
of shifting production or sourcing from a conventional system to an alternative
system.
Because Poore and Nemecek’s (2018) database only captured studies published between
2000 and June 2016, we performed a literature review using similar search terms
and study inclusion criteria to capture additional studies that were published
through 2022. As Poore and Nemecek (2018) did, in some instances we performed
adjustments to fill data gaps or make results more comparable between studies
(e.g., estimating land use using data included in a study, making assumptions
to estimate impacts from the animals’ full life cycle). See Appendix A for more
details on our approach to adding in more recent studies and Appendix B for the
full list of “paired studies” included in our analysis below, as well as all adjustments
made. The Glossary provides definitions of the various production systems.
For each quantitative environmental indicator (e.g., GHG emissions, land use)
in each “paired study,” we calculated the percent changes that occurred when shifting
from the conventional system to the alternative production system.'
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals Finally, there are more complex nutrient quality indices
that could be used as denominators (FAO 2021; Katz-Rosene et al. 2023), but, since
no consensus exists about which one is “best,” we have used the simpler denominator
of protein. In sum, use of any of these alternative numerators and denominators
would not change the main findings and recommendations of this report.
4. For GHG emissions, we removed land-use-change emissions from the estimates
in Poore and Nemecek (2018), so as not to double-count with the “carbon opportunity
costs” of agricultural land use.'
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals Shift toward lower-emissions foods. As noted elsewhere
in this report, because beef is an emissions-intensive food, shifting purchases
and sales toward lower-emissions foods can help companies reduce scope 3 emissions.
There is growing interest in improving grazing management to increase the amount
of carbon sequestered in pasturelands, a practice often called “regenerative grazing.”
Some proponents of regenerative grazing even suggest that by removing carbon from
the atmosphere, soil carbon sequestration could fully offset GHG emissions from
beef production, suggesting potentially “carbon neutral” or “carbon negative”
beef. And while traditional life cycle assessments assumed that soil carbon stocks
on agricultural lands were in equilibrium and did not include soil carbon stock
changes in studies on agriculture’s environmental impacts, more recent studies
have begun to incorporate soil carbon measurements, including several beef studies
included in our review (Buratti et al. 2017; Eldesouky et al. 2018; Stanley et
al. 2018).'
- source_sentence: what is the npv for land restoration in latin america
sentences:
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals Overall, this places fish and seafood at the lower end
of the environmental impact spectrum for animal proteins (Gephart et al. 2021)
but usually still higher than plant-based proteins.
Similarly to terrestrial animal proteins, life cycle assessments of aquaculture
(fish farming) have found that there are environmental trade-offs with intensification.
When finfish and crustacean aquaculture systems move along the spectrum from more
traditional extensive systems to more industrialized intensive systems, land use
and water use per kilogram of fish declines, but water pollution and energy use
per kilogram of fish grow (Bohnes et al. 2018; Waite et al. 2014; Hall et al.
2011). Effects on GHG emissions can be mixed under intensification due to the
growth in energy use and land use for feeds balanced by the reduction in land
use for ponds (Searchinger et al. 2019), and translation of land use into “carbon
opportunity costs” can help better weigh these trade-offs. Aquaculture is also
a significant user of wild fish as feed; more than 20 percent of total wild-caught
fish catch in 2020 went to “nonfood” uses—mostly for fishmeal and fish oil used
in aquaculture operations (FAO 2022c).'
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals ▪ The company first simulates a pure “less meat” strategy
to reduce scope 3 emissions and carbon opportunity costs by a combined 25 percent.
To do so, it finds that sourcing 50 percent less beef, 20 percent less of other
meats, and 15 percent less dairy—and shifting the purchases toward pulses, soy,
and vegetables—achieves this 25 percent reduction in climate impacts.
▪ The company then explores a plausible scenario of shifting all chicken and egg
purchases toward higherwelfare products. It uses Table 7 and selects points within
the impact ranges to assume that free-range chicken and eggs could lead to 15
percent higher GHG emissions and 25 percent higher land use (carbon opportunity
costs) than conventional chicken. The company estimates that this would increase
total climate impacts, but only slightly, since chicken and eggs represent a small
amount of the company’s total climate impact. Under this scenario, total climate
impacts are reduced versus the base year by “only” 24 percent instead of 25 percent.
▪'
- The Economic Case for Landscape Restoration in Latin America This implies an underestimation
of benefits given that, in this form, the restoration scenario equation does not
account for the remaining annual difference in net flow values between the degraded
hectare that is restored and the same hectare left degraded for the years between
full restoration and the end of the study’s overall assumed 50-year time horizon.
The NPVs of all target hectares would have to be calculated for all 50 years,
particularly in the cases of lightly and moderately degraded lands which have
recovery periods under restoration (delimited in this equation by t, which are
only 7 and 15 years, respectively).
- source_sentence: what is meat sourcing strategy
sentences:
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals CHAPTER 1 Introduction and context Meat and dairy production
are responsible for a large proportion of global greenhouse gas (GHG) emissions.
According to one widely cited estimate by the Food and Agriculture Organization
of the United Nations (FAO), animal agriculture (including the agricultural production
process and related land-use change) accounted for 14.5 percent of global GHG
emissions in 2005, with beef production alone accounting for 6 percent of global
emissions (Gerber and FAO 2013). Toward “Better” Meat? Aligning meat sourcing
strategies with corporate climate and sustainability goals | 11
More recent estimates for animal agriculture’s contribution to global emissions
in 2010–15 are of a similar magnitude, ranging from 11 to 20 percent (e.g., Poore
and Nemecek 2018; Twine 2021; Xu et al. 2021; FAO 2022a). Animal agriculture also
accounted for more than 30 percent of global methane emissions in 2017 (CCAC and
UNEP 2021).'
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals Further work is necessary to gather publicly available
data on other environmental, social, and economic attributes of “better meat,”
such as for soil health, on-farm biodiversity, and agricultural livelihoods, to
inform corporate decision-making. Similarly, better data are needed on alternative
systems and practices related to fish and seafood production; these “blue foods”
are important contributors to global food and nutrition security, but data are
even scarcer for these food production systems than for terrestrial animal agriculture.
In an ideal world, “better meat” production could lead to improvements across
all sustainability goals; however, our analysis shows that companies with quantitative
sustainability goals need to consider both co-benefits and trade-offs across all
goals when designing their meat sourcing strategies. We also show that balancing
these goals is eminently possible. This analysis also confirms the critical importance
of shifting diets high in animal-based foods toward plant-based foods and alternative
proteins to improve both environmental and animal welfare outcomes.'
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals It is true that poultry has a lower climate impact per
kilogram of protein than beef and lamb, and climate strategies may consider a
shift in purchasing from beef toward chicken to continue to provide the same amount
of meat to consumers while reducing GHG emissions. However, an important trade-off
to consider from an animal welfare perspective is the number of animal lives per
unit of protein produced. While alternative systems thought of as “better” might
improve the quality of life of the animals to some degree, animal welfare experts
also recognize the inherent value of all animals, and companies might choose to
factor the number of animals slaughtered into their decision-making as a simple
and easily understood indicator of animal welfare.
Figure 5 shows the trade-off between climate and animal welfare indicators when
shifting between animal-based foods, showing that the foods with the highest climate
impact per kg of protein also require the fewest animals to be killed, and vice
versa. For example, to produce a kg of protein, more than 100 times as many chickens
need to be slaughtered compared to cows.'
- source_sentence: cost of restoring landscape
sentences:
- 'The Economic Case for Landscape Restoration in Latin America This report assesses
the economic costs and benefits of landscape restoration in Latin America and
the Caribbean by monetizing a set of benefits that could flow from 20 million
hectares of restored lands. The introduction highlights some of the drivers and
impacts of degradation in the Latin America and Caribbean region. The section
that follows presents an overview of the method used to monetize the benefits
of landscape restoration; detailed descriptions of the methodology and modeling
approach are available in the annexes. Next, we present the results—the estimation
of net economic benefits from restoration and the different values for biomes
and degree of restoration. Finally, we suggest areas where future analysis could
provide more location-specific financial estimates.
Agriculture and forestry play an important role in the economy and social fabric
of Latin America and the Caribbean'
- 'Getting Ready Include a more comprehensive analysis of the legal framework for
tenure and existing conditions on-the-ground
Discuss how tenure conflicts might be addressed as part of the REDD+ strategy
Discusses the ability of forest agencies to plan and implement forest management
activities Considers the role of non-government stakeholders, including communities,
in forest management Links identified governance challenges to proposed REDD+
strategy options and implementation framework
The NPD provides an overview of recent efforts to improve forest management in
RoC, e.g., through the FLEGT Voluntary Partnership Agreement (VPA), certification
schemes, and improving the coverage and management of protected areas. According
to the NPD, the FLEGT process identified numerous forest sector challenges that
should be addressed as part of a REDD+ program, notably lack of forest administration
capacity and the need to strengthen involvement of local populations in forest
management decision-making. According to the NPD, over 4 million hectares of concessions
have been developed since 2001, but the NPD does not discuss the role of the private
sector in forest management activities in detail.'
- The Economic Case for Landscape Restoration in Latin America Nevertheless, because
E&M activities will always require more than a single year to be fully implemented,
the full per hectare cost should not be assigned to the first year of restoration
alone, but rather to a number of initial years along the restoration time horizon.
In the case of lightly degraded landscapes, the total cost/ha (from Tables 7 and
8) has been divided and assigned equally to the first four years (or roughly the
first half) of the restoration time horizon. In the case of moderately degraded
lands, the total cost has been subtracted from annual benefit flow values in equal
annual tranches over the first 8 years (again, roughly the first half of the restoration
time horizon). Finally, total costs for severely degraded lands are subtracted
in equal annual amounts over the first 25 years of the restoration time horizon.
Allocating costs over a 25-year time horizon has the effect of discounting costs
relative to the benefits.
- source_sentence: what is the wri meat initiative?
sentences:
- 'Pilot analysis of global ecosystems: Grassland ecosystems Although GLASOD was
by necessity a somewhat subjective assessment it was extremely carefully prepared
by leading experts in the field. It remains the only global database on the status
of human-induced soil degradation, and no other data set comes as close to defining
the extent of desertification at the global scale (UNEP 1997: V).'
- 'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate
and Sustainability Goals Toward “Better” Meat? Aligning meat sourcing strategies
with corporate climate and sustainability goals
WOR L D WOR L D R E S O U R C E S R E S O U R C E S I NS T I T U T E I NS T I
T U T E
RICHARD WAITE is the Acting Director for Agriculture Initiatives at WRI.
is a doctoral student with Oxford University’s Environmental Change Institute
and a former Research Analyst for WRI’s Food and Climate Programs.
CLARA CHO is the Data Analyst for the Coolfood initiative at WRI. Contact: clara.cho@wri.org.
We are pleased to acknowledge our institutional strategic partners that provide
core funding to WRI: the Netherlands Ministry of Foreign Affairs, Royal Danish
Ministry of Foreign Affairs, and Swedish International Development Cooperation
Agency.
The authors acknowledge the following individuals for their valuable guidance
and critical reviews:'
- 'Getting Ready THE IMPORTANCE OF FOREST GOVERNANCE TO THE REDD+ READINESS PROCESS
Strengthening forest governance will be an essential component of the activities
implemented by countries seeking to achieve significant and lasting emission reductions
through REDD+. Poor forest governance is often characterized by weak capacity
to manage natural resources, lack of decision-maker accountability to impacted
stakeholders, and lack of public access to information about the status and use
of forest resources. Potential drivers of deforestation and forest degradation—such
as illegal logging, unplanned forest conversion, and conflicts over access to
land and resources—are often symptoms of weak forest governance. To develop effective
national REDD+ strategies, governments need to better understand these challenges
and develop measures to strengthen forest governance in ways that build the trust
of domestic and international stakeholders.'
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 BAAI/bge-m3
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: ir eval
type: ir-eval
metrics:
- type: cosine_accuracy@1
value: 0.34030612244897956
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5389030612244898
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6211734693877551
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7122448979591837
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34030612244897956
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17963435374149658
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12423469387755101
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07122448979591836
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.34030612244897956
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5389030612244898
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6211734693877551
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7122448979591837
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5191028810993514
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.458020782717851
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.46727356494811056
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
### 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("collaborativeearth/bge-m3_wri")
# Run inference
sentences = [
'what is the wri meat initiative?',
'Toward "Better" Meat? Aligning Meat Sourcing Strategies with Corporate Climate and Sustainability Goals Toward “Better” Meat? Aligning meat sourcing strategies with corporate climate and sustainability goals\n\nWOR L D WOR L D R E S O U R C E S R E S O U R C E S I NS T I T U T E I NS T I T U T E\n\nRICHARD WAITE is the Acting Director for Agriculture Initiatives at WRI.\n\nis a doctoral student with Oxford University’s Environmental Change Institute and a former Research Analyst for WRI’s Food and Climate Programs.\n\nCLARA CHO is the Data Analyst for the Coolfood initiative at WRI. Contact: clara.cho@wri.org.\n\nWe are pleased to acknowledge our institutional strategic partners that provide core funding to WRI: the Netherlands Ministry of Foreign Affairs, Royal Danish Ministry of Foreign Affairs, and Swedish International Development Cooperation Agency.\n\nThe authors acknowledge the following individuals for their valuable guidance and critical reviews:',
'Pilot analysis of global ecosystems: Grassland ecosystems Although GLASOD was by necessity a somewhat subjective assessment it was extremely carefully prepared by leading experts in the field. It remains the only global database on the status of human-induced soil degradation, and no other data set comes as close to defining the extent of desertification at the global scale (UNEP 1997: V).',
]
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]
```
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `ir-eval`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3403 |
| cosine_accuracy@3 | 0.5389 |
| cosine_accuracy@5 | 0.6212 |
| cosine_accuracy@10 | 0.7122 |
| cosine_precision@1 | 0.3403 |
| cosine_precision@3 | 0.1796 |
| cosine_precision@5 | 0.1242 |
| cosine_precision@10 | 0.0712 |
| cosine_recall@1 | 0.3403 |
| cosine_recall@3 | 0.5389 |
| cosine_recall@5 | 0.6212 |
| cosine_recall@10 | 0.7122 |
| **cosine_ndcg@10** | **0.5191** |
| cosine_mrr@10 | 0.458 |
| cosine_map@100 | 0.4673 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 82,169 training samples
* Columns: question and answer
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 4 tokens
- mean: 10.62 tokens
- max: 31 tokens
| - min: 53 tokens
- mean: 232.17 tokens
- max: 337 tokens
|
* Samples:
| question | answer |
|:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| what is the economic case of restoration | The Economic Case for Landscape Restoration in Latin America The Economic Case for Landscape Restoration in Latin America
THE ECONOMIC CASE FOR LANDSCAPE RESTORATION IN LATIN AMERICA
WALTER VERGARA, LUCIANA GALLARDO LOMELI, ANA R. RIOS, PAUL ISBELL, STEVEN PRAGER, RONNIE DE CAMINO
Land use and land-use change are central to the economic and social fabric of Latin America and the Caribbean, and essential to the region’s prospects for sustainable development. Countries are realizing that now, more than ever, is the time for action. Eleven countries, three Brazilian states and several regional programs have already committed to restoring more than 27 million hectares of degraded land in Latin America—but can these ambitions become a reality while supporting good living standards and economic development? |
| economic case of landscape restoration in latin america | The Economic Case for Landscape Restoration in Latin America The Economic Case for Landscape Restoration in Latin America
THE ECONOMIC CASE FOR LANDSCAPE RESTORATION IN LATIN AMERICA
WALTER VERGARA, LUCIANA GALLARDO LOMELI, ANA R. RIOS, PAUL ISBELL, STEVEN PRAGER, RONNIE DE CAMINO
Land use and land-use change are central to the economic and social fabric of Latin America and the Caribbean, and essential to the region’s prospects for sustainable development. Countries are realizing that now, more than ever, is the time for action. Eleven countries, three Brazilian states and several regional programs have already committed to restoring more than 27 million hectares of degraded land in Latin America—but can these ambitions become a reality while supporting good living standards and economic development? |
| what is lata-american landscape | The Economic Case for Landscape Restoration in Latin America Agriculture and forestry exports from Latin America represent about 13 percent of the global trade of food, feed, and fiber and account for a majority of employment outside large urban areas—numbers only expected to grow as Latin America is called upon to meet an increasing global demand for food. Yet, since the turn of the century, about 37 million hectares of natural forests, savannas and wetlands have been transformed to expand agriculture. Cumulative, unsustainable land-use practices have led to the degradation of about 300 million hectares, resulting in a reduction in yields and quality of production, and in losses in biomass content, soil quality, surface water hydrology, and biodiversity. Deforestation, land-use change, and unsustainable agricultural activities are also currently the largest drivers of climate change in the region, accounting for 56 percent of all greenhouse gas emissions. Today, while some progress ha... |
* 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`: 32
- `learning_rate`: 1e-06
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True
- `gradient_checkpointing`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `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`: 8
- `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`: 1e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 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}
- `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`: True
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional