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README.md
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# Model Card for Model ID
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## Model Details
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### Model Description
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.10.1.dev0
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# Model Card for Model ID
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This model provides a ready-to-go temporal expression normalization tool.
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## Model Details
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### Model Description
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We present a series of multilingual temporal expression normalization models. Proposed in the {paper}.
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This model has been trained over Timebank and E3C multilingual corpora with the sole objevtive of normalizing temporal expressions following the ISO TimeML schema.
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A temporal expression is classified into four types: Date, Time, Duration and Set. This model predicts the value of a temporal expression.
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For example for the expression of type duration "8 hours" the model would predict the value PT8H.
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- **Developed by:** Alejandro Sánchez de Castro, Lourdes Araujo and Juan Martínez-Romo
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- **Language(s) (NLP):** Multilingual
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## Uses
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### Downstream Use [optional]
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For using the model just follow the general PEFT guide. When using the model please use the following prompt:
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Given the temporal expression type the reference date, the temporal expression and the context phrase in which the temporal expression appears, generate the value according to the TIMEX3 scheme. In the context phrase the temporal expression appears, sometimes it will be neccesary to pay attention to the surrounding context in order to resolve the expression.
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###Context phrase:
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{sentence}
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###Temporal expression type:
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{type}
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###Temporal expression:
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{expression}
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###Reference date
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{dct}
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###Value:
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The model will then return a sequence with the predicted value. It can be extracted through a simple pattern match.
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## Evaluation
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We compare our proposed models against current multilingual solutions as shown in the {paper}
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### Results
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We managed to get an outstanding performance on over 7 languages and a promising zero-shot performance over non-trained languages.
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#### Summary
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## Citation [optional]
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To be defined
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**BibTeX:**
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**APA:**
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To be defined
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### Acklowledge:
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- **Funded by:** funded by the following projects DOTT-HEALTH (MCI/AEI/FEDER, UE with identification PID2019-106942RB-C32), OBSER-MENH (MCIN/AEI/10.13039/501100011033 and NextGenerationEU”/PRTR with identification TED2021-130398B-C21), SICAMESP (with identification 2023-VICE-0029) and by the project EDHER-MED (with identification PID2022-136522OB-C21)"
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### Model Sources [optional]
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- **Paper:** To be defined
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## Model Card Contact
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asanchez@lsi.uned.es
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### Framework versions
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- PEFT 0.10.1.dev0
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