Update README.md

README.md

@@ -1,199 +1,190 @@
 ---
 library_name: transformers
-tags: []
+tags:
+  - sequence-classification
+  - text-classification
+  - nli
+  - xlm-roberta
+  - vietnamese
+  - kaggle
 ---
 
-# Model Card for Model ID
+# XLM-RoBERTa-base fine-tuned for Vietnamese NLI
 
-<!-- Provide a quick summary of what the model is/does. -->
+A Vietnamese Natural Language Inference (NLI) model that predicts the relation between a **premise** and a **hypothesis** as one of:
+- `c` (contradiction)
+- `n` (neutral)
+- `e` (entailment)
 
+This model fine-tunes **xlm-roberta-base** using a stratified 80/10/10 split, optimized to run on a single GPU (Kaggle T4/P100).
 
+---
 
 ## 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):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Developed by:** Lê Lý (MoMo Talent 2025)
+- **Model type:** XLM-RoBERTa encoder for sequence classification (3 labels)
+- **Languages:** Vietnamese (vi)
+- **License:** Inherits from upstream **xlm-roberta-base** (set the model page license accordingly)
+- **Finetuned from:** `xlm-roberta-base`
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
+- **Base model:** XLM-RoBERTa (Conneau et al., 2020)
+- **Training script:** Included below in this card (Kaggle-ready)
 
-- **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
+- Vietnamese NLI inference for research, demos, or as a component in larger systems (e.g., retrieval/ranking, dialog consistency checks).
 
-<!-- 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]
+### Downstream Use
+- Fine-tune further on domain-specific VN NLI or related tasks (stance detection, contradiction detection in QA/assistants).
 
 ### Out-of-Scope Use
+- Non-VN text without adaptation.
+- Safety-critical decisions without human oversight.
+- Open-domain factual verification (this is NLI, not a fact-checker).
 
-<!-- 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. -->
+- Trained on a VN NLI dataset; distributional shift (domain, register, slang, figurative language) may degrade performance.
+- NLI labels can be sensitive to annotation style/instructions; avoid over-interpreting borderline cases.
 
-[More Information Needed]
+**Recommendations:** Evaluate on your target domain; monitor confusion between `n` vs `e`/`c`; consider calibration or thresholding if used in pipelines.
 
-### Recommendations
+---
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## How to Get Started
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-## How to Get Started with the Model
+model_id = "YOUR_USERNAME/xlmr-vinli-finetune"  # replace with your repo id
+tok = AutoTokenizer.from_pretrained(model_id)
+mdl = AutoModelForSequenceClassification.from_pretrained(model_id)
 
-Use the code below to get started with the model.
+id2label = mdl.config.id2label  # {0:'c',1:'n',2:'e'}
+text = {"premise": "Trời đang mưa rất to.", "hypothesis": "Bên ngoài khô ráo và không có mưa."}
 
-[More Information Needed]
+enc = tok(text["premise"], text["hypothesis"], return_tensors="pt", truncation=True, max_length=256)
+with torch.no_grad():
+    logits = mdl(**enc).logits
+pred = logits.softmax(-1).argmax(-1).item()
+print("Prediction:", id2label[pred])
+```
 
 ## 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. -->
-
-[More Information Needed]
-
-### Training Procedure
+### Data
+- **Path (Kaggle):** `/kaggle/input/nli-vietnam/full_data_true.json`
+- **Labels:** `{"c":0, "n":1, "e":2}`
+- **Split:** Stratified ~80/10/10 (train/val/test)
+
+*Ensure JSON has fields: `id`, `premise`, `hypothesis`, `label` (labels in `{c,n,e}`).*
+
+### Procedure
+
+**Preprocessing**
+- **Tokenizer:** `XLMRobertaTokenizerFast` (max_length=256, truncation)
+
+**Hyperparameters**
+- **Epochs:** 4
+- **Optim:** AdamW (via HF Trainer)
+- **LR:** 2e-5
+- **Weight decay:** 0.01
+- **Warmup ratio:** 0.06
+- **Scheduler:** linear
+- **Batch:** `per_device_train_batch_size=8`, `per_device_eval_batch_size=32`
+- **Grad Accumulation:** 2 (effective train batch ~16)
+- **Precision:** `bf16` if available (Ampere+), else `fp16`
+- **Label smoothing:** 0.05
+- **Early stopping:** patience 2
+- **Gradient checkpointing:** enabled
+- `save_safetensors=True`, `load_best_model_at_end=True` on `f1_macro`
+
+### Compute
+- **Hardware:** Single NVIDIA T4/P100 16GB (Kaggle)
+- `dataloader_num_workers=2`, `pin_memory=True`
+
+### Speeds, Sizes, Times
+- **Checkpoint size:** standard `xlm-roberta-base` head (+classifier)
+- *Exact wall-clock depends on GPU; typical Kaggle session completes within normal time limits.*
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### 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
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+### Metrics & Factors
+- **Metrics:** Accuracy, Macro F1
+- **Factors:** Per-label performance (c, n, e)
+
+### Results (Test)
+```yaml
+Accuracy: 0.9901
+Macro F1: 0.9878
+Support: 1113 samples (c=429, n=108, e=576)
+```
+
+**Classification Report:**
+```
+              precision    recall  f1-score   support
+
+           c     0.9930    0.9883    0.9907       429
+           n     0.9815    0.9815    0.9815       108
+           e     0.9896    0.9931    0.9913       576
+
+weighted avg     0.9901    0.9901    0.9901      1113
+```
+
+**Confusion Matrix:**
+```[[424   0   5],
+ [  1 106   1],
+ [  2   2 572]]
+```
+*Note: Replicate numbers may vary slightly due to randomness/hardware.*
+
+### Environmental Impact
+- **Hardware:** Single T4/P100 16GB (Kaggle)
+- **Cloud Provider/Region:** Kaggle (unspecified)
+- **Hours used:** Not logged
+- **Carbon Emitted:** Not estimated
+  - *You can estimate with the [MLCO2 Impact calculator](https://mlco2.github.io/impact#compute).*
 
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### 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
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+---
 
-[More Information Needed]
+## Technical Specifications
 
-## More Information [optional]
+### Architecture & Objective
+- **Backbone:** XLM-RoBERTa Base
+- **Head:** Linear classification (3 labels)
+- **Objective:** Cross-entropy with label smoothing (0.05); optional class weighting (off by default)
 
-[More Information Needed]
+### Software
+- `transformers==4.43.3`
+- `datasets==2.21.0`
+- `accelerate==0.33.0`
+- `evaluate==0.4.2`
+- `scikit-learn==1.5.1`
+- `torch` (CUDA)
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## Citation
 
-## Model Card Contact
+### XLM-RoBERTa
+```bibtex
+@inproceedings{conneau2020unsupervised,
+  title={Unsupervised Cross-lingual Representation Learning at Scale},
+  author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
+  booktitle={ACL},
+  year={2020}
+}
+```
 
-[More Information Needed]
+## Contact
+**Author:** Lê Lý