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README.md

@@ -1,137 +1,59 @@
 ---
+library_name: transformers
+license: apache-2.0
+base_model: phonghoccode/ALQAC_2025_Reranker_top20_zalo
 tags:
-- sentence-transformers
-- cross-encoder
-- reranker
-pipeline_tag: text-ranking
-library_name: sentence-transformers
+- generated_from_trainer
+model-index:
+- name: ALQAC_2025_Reranker_top20
+  results: []
 ---
 
-# CrossEncoder
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model trained using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+# ALQAC_2025_Reranker_top20
 
-## Model Details
+This model is a fine-tuned version of [phonghoccode/ALQAC_2025_Reranker_top20_zalo](https://huggingface.co/phonghoccode/ALQAC_2025_Reranker_top20_zalo) on an unknown dataset.
 
-### Model Description
-- **Model Type:** Cross Encoder
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 8192 tokens
-- **Number of Output Labels:** 1 label
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+## Model description
 
-### Model Sources
+More information needed
 
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+## Intended uses & limitations
 
-## Usage
+More information needed
 
-### Direct Usage (Sentence Transformers)
+## Training and evaluation data
 
-First install the Sentence Transformers library:
+More information needed
 
-```bash
-pip install -U sentence-transformers
-```
+## Training procedure
 
-Then you can load this model and run inference.
-```python
-from sentence_transformers import CrossEncoder
+### Training hyperparameters
 
-# Download from the 🤗 Hub
-model = CrossEncoder("phonghoccode/ALQAC_2025_Reranker_top20")
-# Get scores for pairs of texts
-pairs = [
-    ['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
-    ['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
-    ['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
-]
-scores = model.predict(pairs)
-print(scores.shape)
-# (3,)
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 8
+- total_eval_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3.0
+- mixed_precision_training: Native AMP
 
-# Or rank different texts based on similarity to a single text
-ranks = model.rank(
-    'How many calories in an egg',
-    [
-        'There are on average between 55 and 80 calories in an egg depending on its size.',
-        'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
-        'Most of the calories in an egg come from the yellow yolk in the center.',
-    ]
-)
-# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
-```
+### Training results
 
-<!--
-### Direct Usage (Transformers)
 
-<details><summary>Click to see the direct usage in Transformers</summary>
 
-</details>
--->
+### Framework versions
 
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Framework Versions
-- Python: 3.10.12
-- Sentence Transformers: 5.0.0
-- Transformers: 4.53.2
-- PyTorch: 2.7.0+cu126
-- Accelerate: 1.8.1
-- Datasets: 4.0.0
-- Tokenizers: 0.21.2
-
-## Citation
-
-### BibTeX
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
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-## Model Card Contact
-
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+- Transformers 4.53.2
+- Pytorch 2.7.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.2