Update README.md

README.md

@@ -4,106 +4,83 @@ language:
 license: apache-2.0
 base_model: google/flan-t5-small
 tags:
-- generated_from_trainer
+- text-simplification
+- paraphrase
+- natural-language-processing
 datasets:
-- sentence-paraphrases
-model-index:
-- name: flan-t5-small-simplifier
-  results: []
+- agentlans/sentence-paraphrases
 ---
+# FLAN-T5 Small Simplifier
 
-<!-- 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. -->
+A fine-tuned text simplification and paraphrasing model based on Google's FLAN-T5 Small, designed to enhance text readability while preserving core semantic meaning.
 
-# flan-t5-small-simplifier
+## Model Details
 
-For paraphrasing and simplifying English text.
+- **Base Model**: [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)
+- **Task**: Text Simplification and Paraphrasing
+- **Languages**: English
 
-Fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the agentlans/sentence-paraphrases dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.1518
-- Num Input Tokens Seen: 32939232
+## Capabilities
 
-## Intended uses & limitations
+The model is specialized in:
+- Reducing text complexity
+- Generating more readable paraphrases
+- Maintaining original semantic content
 
-Works best on sentence length texts.
+## Intended Use
 
-```
-import torch
-from transformers import pipeline
-
-# Check if GPU is available
-device = 0 if torch.cuda.is_available() else -1
-
-# Initialize the pipeline
-model_name = "agentlans/flan-t5-small-simplifier"
-flan_t5_pipeline = pipeline("text2text-generation", model=model_name, device=device)
-
-# Example input
-input_text = "While navigating the labyrinthine corridors of epistemological uncertainty, the precocious philosopher—whose seminal work on phenomenological interpretation had already garnered significant academic acclaim—paused momentarily to contemplate the intricate interplay between subjective perception and objective reality, ultimately recognizing that the boundaries of human understanding are perpetually fluid and dynamically reconstructed through continuous intellectual discourse and empirical investigation."
-
-# Generate output
-output = flan_t5_pipeline(input_text, max_length=1024)
-
-# Print the result
-print(output[0]["generated_text"])
-# The precocious philosopher, who had already been a major academic acclaim for his seminal work on phenomenological interpretation, paused momentarily to contemplate the intricate interplay between subjective perception and objective reality, recognizing that the boundaries of human understanding are perpetually fluid and dynamically reconstructed through continuous intellectual discourse and empirical investigation.
-```
+**Primary Use Cases**:
+- Academic writing simplification
+- Technical document readability enhancement
+- Content adaptation for diverse audiences
 
-Limitations:
-- English only
-- Doesn't handle mixed language texts well (for example, English with Greek letter words)
-- Might not be able to simplify some texts
+**Limitations**:
+- Optimized for English language texts
+- Best performance on sentence-length inputs
+- May struggle with highly specialized or mixed-language texts
 
-## Training and evaluation data
+## Usage Example
 
-agentlans/sentence-paraphrases
-
-This dataset is a curated collection of sentence-length paraphrases derived from two primary sources:
-
-    humarin/chatgpt-paraphrases
-    xwjzds/paraphrase_collections.
-
-Dataset Details
-Dataset Description
-
-The dataset is structured to provide pairs of sentences from an original text and its paraphrase(s). For each entry:
+```python
+from transformers import pipeline
 
-    The "text" field contains the least readable paraphrase.
-    The "paraphrase" field contains the most readable paraphrase.
+simplifier = pipeline(
+    "text2text-generation", model="agentlans/flan-t5-small-simplifier"
+)
 
-Readability was assessed using the agentlans/deberta-v3-xsmall-zyda-2-readability model.
+complex_text = "While navigating the labyrinthine corridors of epistemological uncertainty, the precocious philosopher paused to contemplate the intricate interplay between subjective perception and objective reality."
 
-## Training procedure
+simplified_text = simplifier(complex_text, max_length=128)[0]["generated_text"]
+print(simplified_text)
 
-### Training hyperparameters
+# The precocious philosopher paused to contemplate the complex interplay between subjective perception and objective reality while navigating the labyrinthine corridors of epistemological uncertainty.
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 2.0
+## Training Details
 
-### Training results
+**Dataset**: [agentlans/sentence-paraphrases](https://huggingface.co/datasets/agentlans/sentence-paraphrases)
+- Source: Curated paraphrase collections
+- Readability assessment using a finetuned [DeBERTa v3 XSmall](https://huggingface.co/agentlans/deberta-v3-xsmall-zyda-2-readability)
 
-| Training Loss | Epoch  | Step  | Validation Loss | Input Tokens Seen |
-|:-------------:|:------:|:-----:|:---------------:|:-----------------:|
-| 1.4423        | 0.2224 | 10000 | 1.2431          | 3655312           |
-| 1.3884        | 0.4448 | 20000 | 1.2093          | 7331520           |
-| 1.3782        | 0.6673 | 30000 | 1.1859          | 10990432          |
-| 1.3595        | 0.8897 | 40000 | 1.1787          | 14653328          |
-| 1.3059        | 1.1121 | 50000 | 1.1665          | 18326104          |
-| 1.3298        | 1.3345 | 60000 | 1.1589          | 21991016          |
-| 1.2994        | 1.5569 | 70000 | 1.1562          | 25656600          |
-| 1.2952        | 1.7794 | 80000 | 1.1518          | 29314808          |
+**Training Hyperparameters**:
+- Learning Rate: 5e-05
+- Batch Size: 8
+- Optimizer: Adam
+- Epochs: 2.0
 
+**Performance Metrics**:
+| Epoch | Training Loss | Validation Loss |
+|:-----:|:-------------:|:---------------:|
+| 0.22  | 1.4423        | 1.2431          |
+| 0.89  | 1.3595        | 1.1787          |
+| 1.78  | 1.2952        | 1.1518          |
 
-### Framework versions
+## Framework
 
 - Transformers 4.43.3
-- Pytorch 2.3.0+cu121
+- PyTorch 2.3.0+cu121
 - Datasets 3.2.0
-- Tokenizers 0.19.1
+
+## Ethical Considerations
+
+Users should review generated text for accuracy and appropriateness, as the model may inherit biases from training data.