Update README.md

README.md

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 <!-- Provide a quick summary of what the model is/does. -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+This modelcard documents FM-FCI/DateArith-VLSP2025, a Vietnamese LLM fine-tuned for duration question answering task. It achieved #4 in the VLSP 2025 benchmark for date-arith task.
 
 ## Model Details
 
@@ -16,185 +16,58 @@ This modelcard aims to be a base template for new models. It has been generated
 
 <!-- Provide a longer summary of what this model is. -->
 
+This work investigates two subtasks in temporal reasoning: 1. Date Arithmetic (datearith) and 2. Duration Question Answering (durationQA). For date-arith, we focus on finetuning large language models (LLMs) to directly extract and compute answers. For durationQA, the challenge lies in identifying both explicit and implicit duration expressions in text and reasoning with world knowledge to assess correctness. We explore multiple approaches, from naive supervised fine-tuning (SFT) to SFT augmented with reasoning-based synthetic data and GRPO. Our findings highlight the critical role of carefully constructed data and appropriate training strategies in enabling effective temporal reasoning.
 
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-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** FPT Smart Cloud, FPT Corporation
+- **Model type:** Dense
+- **Language(s) (NLP):** Vietnamese (primary)
+- **License:** ?
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **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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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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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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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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]
+- **Repository:** https://github.com/duccd4/vlsp2025-temporal-qa
+- **Paper:** Enabling Temporal Commonsense in Vietnamese LLMs – Date-Arith and DurationQA
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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]
+15,000 samples
 
 ### Training Procedure
 
-<!-- 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
 
-- **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]
+Hyperparameter SFT GRPO
+Attention FlashAttention-2 FlashAttention-2
+Batch size / device 64 16
+Learning rate 5.0e-5 1.0e-6
+Epochs 3 5
+Optimizer AdamW AdamW
+DeepSpeed config ZeRO-3 ZeRO-3
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
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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
+### Testing Data
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Đánh giá dựa vào public test, private test mà BTC cung cấp
 
-[More Information Needed]
+### Metrics
 
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
+F1, P, R, EM
 
 ### Results
 
-[More Information Needed]
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-#### Summary
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-
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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]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **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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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+The Engineers 81.89 76.45 88.15 47.52
+UIT_BlackCoffee 80.13 73.06 88.72 42.72
+AI5 80.03 74.79 86.06 49.12
+HUET 79.97 70.71 92.02 40.32
+Softmind_AIO 79.06 70.28 90.33 34.08
 
 **BibTeX:**
 
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-**APA:**
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-## Glossary [optional]
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-<!-- 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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-[More Information Needed]
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-## Model Card Contact
+Enabling Temporal Commonsense in Vietnamese LLMs – Date-Arith and DurationQA
 
-[More Information Needed]
+Duc Dinh Chu*, Thanh-Bac Nguyen Ba*, Duy Dinh Le, Khanh Van Tran