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---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Validation Loss and Accuracy report:
* **Validation Loss:** 0.11179830832788
* **Validation Accuracy:** 0.9352647152068487
## Classification Report
| | **Precision** | **Recall** | **F1-Score** | **Support** |
|-------------------|-----------|--------|----------|---------|
| commodity | 0.78 | 0.73 | 0.75 | 86 |
| company | 0.76 | 0.80 | 0.78 | 230 |
| delivery_location| 0.65 | 0.41 | 0.50 | 32 |
| delivery_port | 0.69 | 0.89 | 0.78 | 309 |
| delivery_state | 0.71 | 0.63 | 0.67 | 82 |
| incoterms | 0.77 | 0.88 | 0.82 | 117 |
| measures | 0.77 | 0.84 | 0.80 | 629 |
| package_type | 0.95 | 0.94 | 0.95 | 286 |
| pickup_cap | 0.86 | 0.93 | 0.90 | 107 |
| pickup_location | 0.71 | 0.77 | 0.74 | 356 |
| pickup_port | 0.45 | 0.42 | 0.43 | 12 |
| pickup_state | 0.68 | 0.75 | 0.71 | 71 |
| quantity | 0.78 | 0.91 | 0.84 | 154 |
| stackable | 0.94 | 0.98 | 0.96 | 61 |
| total_quantity | 0.86 | 0.60 | 0.71 | 10 |
| total_volume | 0.86 | 0.46 | 0.60 | 13 |
| total_weight | 0.68 | 0.81 | 0.74 | 136 |
| volume | 0.60 | 0.72 | 0.65 | 43 |
| weight | 0.67 | 0.58 | 0.62 | 114 |
| | | | | |
| Micro Avg | 0.76 | 0.82 | 0.79 | 2848 |
| Macro Avg | 0.75 | 0.74 | 0.73 | 2848 |
| Weighted Avg | 0.76 | 0.82 | 0.79 | 2848 |
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** Italian/English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** microsoft/deberta-base
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
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#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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