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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- bert |
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- Aspects |
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- ABSA |
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- Aspects Extraction |
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- roberta |
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--- |
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# Model Card for Model ID |
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Extracting Implicit and Explicit Aspects from Restaurant Reviews using RoBERTa-Large Variant with Benchmark Efficiency and Custom Dataset |
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We present a groundbreaking approach to extracting implicit and explicit aspects from restaurant reviews in the domain. Leveraging the powerful RoBERTa-Large variant, our method achieves remarkable performance while utilizing a custom dataset. |
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Our research addresses the challenging task of aspect extraction, which involves identifying both explicit aspects explicitly mentioned in reviews, as well as implicit aspects that are indirectly referred to. By employing RoBERTa-Large, a state-of-the-art language model, we leverage its advanced contextual understanding to capture nuanced information from textual data. |
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To ensure the efficiency and accuracy of our approach, we benchmarked our system against existing methods in the field. The results were outstanding, highlighting the superiority of our approach in terms of precision, recall, and overall performance. |
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Furthermore, we developed a custom dataset tailored specifically to the restaurant domain, encompassing a diverse range of reviews from various platforms. This dataset allowed us to train our model with domain-specific knowledge, leading to better aspect extraction outcomes. |
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Overall, our research presents a novel and efficient solution for aspect extraction from restaurant reviews. By employing the RoBERTa-Large variant and a carefully curated custom dataset, we have achieved remarkable results that surpass existing approaches. This breakthrough has significant implications for sentiment analysis, opinion mining, and other natural language processing applications in the restaurant domain. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Ali Haider |
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- **Shared by:** Ali Haider |
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- **Model type:** Bert Varinet |
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- **Language(s) (NLP):** English (Restaurant Domain Reviews) |
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- **Finetuned from model:** Roberta Large |
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## Uses |
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Aspects Extraction Model in Restaurant Domain aimns to extract the Implicit and explicit aspects that might be speifified in the Reviews we can use our model for vairous purposes such as |
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1. Aspects extraction from the reviews Sentences and classification under 34 aspects-categoires. |
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2. Aspects based Restaurant Recommendation system |
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3. Restaurant Reviews Analysis |
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### Out-of-Scope Use |
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Model has been tuned to classifiy the out of scope sentences into the General. |
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## How to Get Started with the Model |
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Sample Sentence: The food was very delicious, elegant Ambience and Decoration , floors were clean and most importantly the food was affoardable. |
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Expected Output: |
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Food-Taste |
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Food-Price |
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Restaurant-Decoration |
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Restaurant-Atmosphere |
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Restaurant-Hygiene |
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## Training Details |
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Roberta-large varient is used with 10678 data entires each of the sentence is classified under serveral Aspects they might belong to and trained till the Validation loss |
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not improving till 3 epochs. |
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### Training Data |
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Reviews are tokenized into sentences and 10678 unique sentences are annotated for training. |
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Aspects are Categorized under 4 categories |
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Restaurants (Restaurants and Ambience Merged) |
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Atmopshere |
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Building |
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Location |
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Features |
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Hygiene |
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Kitchen |
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Recommendation |
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View |
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Decoration |
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Seating Plan |
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Options |
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Experience |
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General |
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Service (Staff and Service Merged) |
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Behavior |
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Wait Time |
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General |
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Experience |
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Food (Food and Drinks Merged) |
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Cuisine |
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Deals |
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Diet Options |
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Ingredients |
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Menu |
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Kitchen |
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Portion |
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Presentation |
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Price |
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Quality |
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Taste |
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Flavor |
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Recommendation |
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Experience |
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Dishes |
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General |
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General (Out of Domain and Contextless Sentences) |
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General |
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#### Training Hyperparameters |
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lr=2e-5 eps=1e-8 batch_size=32 |
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## Evaluation and Results |
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Classification Report |
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precision recall f1-score support |
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FOOD-CUISINE 0.69 0.83 0.76 65 |
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FOOD-DEALS 0.81 0.75 0.78 40 |
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FOOD-DIET_OPTION 0.73 0.93 0.82 71 |
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FOOD-EXPERIENCE 0.38 0.44 0.40 55 |
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FOOD-FLAVOR 0.83 0.94 0.88 63 |
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FOOD-GENERAL 0.65 0.78 0.71 141 |
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FOOD-INGREDIENT 0.77 0.80 0.78 54 |
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FOOD-KITCHEN 0.50 0.60 0.55 35 |
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FOOD-MEAL 0.72 0.74 0.73 208 |
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FOOD-MENU 0.80 0.89 0.84 136 |
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FOOD-PORTION 0.90 0.91 0.90 76 |
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FOOD-PRESENTATION 0.82 0.94 0.87 33 |
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FOOD-PRICE 0.74 0.88 0.80 57 |
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FOOD-QUALITY 0.61 0.66 0.63 102 |
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FOOD-RECOMMENDATION 0.65 0.47 0.55 32 |
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FOOD-TASTE 0.79 0.84 0.82 114 |
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GENERAL-GENERAL 0.98 0.88 0.93 163 |
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RESTAURANT-ATMOSPHERE 0.73 0.79 0.76 170 |
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RESTAURANT-BUILDING 0.90 0.86 0.88 44 |
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RESTAURANT-DECORATION 0.95 0.84 0.89 44 |
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RESTAURANT-EXPERIENCE 0.67 0.60 0.63 189 |
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RESTAURANT-FEATURES 0.55 0.76 0.64 75 |
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RESTAURANT-GENERAL 0.45 0.49 0.47 47 |
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RESTAURANT-HYGIENE 0.94 0.92 0.93 51 |
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RESTAURANT-KITCHEN 0.82 0.85 0.84 33 |
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RESTAURANT-LOCATION 0.59 0.78 0.67 69 |
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RESTAURANT-OPTIONS 0.42 0.41 0.41 32 |
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RESTAURANT-RECOMMENDATION 0.62 0.71 0.67 49 |
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RESTAURANT-SEATING_PLAN 0.78 0.82 0.80 68 |
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RESTAURANT-VIEW 0.80 0.88 0.84 42 |
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SERVICE-BEHAVIOUR 0.65 0.87 0.74 127 |
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SERVICE-EXPERIENCE 0.31 0.24 0.27 21 |
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SERVICE-GENERAL 0.74 0.81 0.77 162 |
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SERVICE-WAIT_TIME 0.86 0.85 0.86 94 |
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micro avg 0.72 0.78 0.75 2762 |
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macro avg 0.71 0.76 0.73 2762 |
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weighted avg 0.73 0.78 0.75 2762 |
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samples avg 0.75 0.78 0.75 2762 |
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Accuracy 0.9801993831240361 |
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Confusin Matrix |
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[[[2047, 24], |
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[ 11, 54]], |
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[[2089, 7], |
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[ 10, 30]], |
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[[2041, 24], |
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[ 5, 66]], |
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[[2041, 40], |
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[ 31, 24]], |
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[[2061, 12], |
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[ 4, 59]], |
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[[1936, 59], |
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[ 31, 110]], |
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[[2069, 13], |
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[ 11, 43]], |
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[[2080, 21], |
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[ 14, 21]], |
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[[1869, 59], |
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[ 55, 153]], |
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[[1969, 31], |
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[ 15, 121]], |
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[[2052, 8], |
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[ 7, 69]], |
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[[2096, 7], |
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[ 2, 31]], |
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[[2061, 18], |
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[ 7, 50]], |
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[[1991, 43], |
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[ 35, 67]], |
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[[2096, 8], |
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[ 17, 15]], |
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[[1997, 25], |
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[ 18, 96]], |
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[[1970, 3], |
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[ 19, 144]], |
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[[1917, 49], |
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[ 36, 134]], |
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[[2088, 4], |
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[ 6, 38]], |
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[[2090, 2], |
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[ 7, 37]], |
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[[1890, 57], |
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[ 75, 114]], |
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[[2015, 46], |
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[ 18, 57]], |
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[[2061, 28], |
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[ 24, 23]], |
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[[2082, 3], |
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[ 4, 47]], |
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[[2097, 6], |
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[ 5, 28]], |
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[[2029, 38], |
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[ 15, 54]], |
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[[2086, 18], |
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[ 19, 13]], |
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[[2066, 21], |
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[ 14, 35]], |
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[[2052, 16], |
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[ 12, 56]], |
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[[2085, 9], |
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[ 5, 37]], |
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[[1950, 59], |
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[ 17, 110]], |
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[[2104, 11], |
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[ 16, 5]], |
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[[1927, 47], |
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[ 30, 132]], |
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[[2029, 13], |
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[ 14, 80]]] |
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Average Validation loss 0.06330019191129883 |
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## Model Card Authors |
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Ali Haider |
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## Model Card Contact |
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+923068983139 |
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[email protected] |
