added model card info

README.md

@@ -44,133 +44,155 @@ library_name: transformers
 ---
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+[roBERTa-base](https://huggingface.co/roberta-base) model was fine-tuned on 50% training English only split of MultiNERD dataset and later evaluated on full test split of the same.   
+The finetuning script can be fetched from [fintuning.py](https://github.com/jayant-yadav/RISE-NER/blob/main/finetuning.ipynb). 
+
+Various other model were tested on the same selection of dataset and the best checkpoint was uploaded. The detailed configuration summary can be found in Appendix section of [report](https://github.com/jayant-yadav/RISE-NER/blob/main/MultiNERD_NER___RISE.pdf).
 
-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).
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
+Head over to [github repo](https://github.com/jayant-yadav/RISE-NER) for all the scripts used to finetune and evalute token-classification model.
+The model is ready to use on [Kaggle](https://www.kaggle.com/datasets/jayantyadav/multinerd-ner-models/) too!
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Jayant Yadav
 
 ## 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. -->
+Token-classification of the following entities are possible:  
+
+| Class | Description | Examples |
+|-------|-------------|----------|
+PER (person) | People | Ray Charles, Jessica Alba, Leonardo DiCaprio, Roger Federer, Anna Massey. |
+ORG (organization) | Associations, companies, agencies, institutions, nationalities and religious or political groups | University of Edinburgh, San Francisco Giants, Google, Democratic Party. |
+LOC (location) | Physical locations (e.g. mountains, bodies of water), geopolitical entities (e.g. cities, states), and facilities (e.g. bridges, buildings, airports). | Rome, Lake Paiku, Chrysler Building, Mount Rushmore, Mississippi River. |
+ANIM (animal) | Breeds of dogs, cats and other animals, including their scientific names. | Maine Coon, African Wild Dog, Great White Shark, New Zealand Bellbird. |
+BIO (biological) | Genus of fungus, bacteria and protoctists, families of viruses, and other biological entities. | Herpes Simplex Virus, Escherichia Coli, Salmonella, Bacillus Anthracis. |
+CEL (celestial) | Planets, stars, asteroids, comets, nebulae, galaxies and other astronomical objects. | Sun, Neptune, Asteroid 187 Lamberta, Proxima Centauri, V838 Monocerotis. |
+DIS (disease) | Physical, mental, infectious, non-infectious, deficiency, inherited, degenerative, social and self-inflicted diseases. | Alzheimer’s Disease, Cystic Fibrosis, Dilated Cardiomyopathy, Arthritis. |
+EVE (event) | Sport events, battles, wars and other events. | American Civil War, 2003 Wimbledon Championships, Cannes Film Festival. |
+FOOD (food) | Foods and drinks. | Carbonara, Sangiovese, Cheddar Beer Fondue, Pizza Margherita. |
+INST (instrument) | Technological instruments, mechanical instruments, musical instruments, and other tools. | Spitzer Space Telescope, Commodore 64, Skype, Apple Watch, Fender Stratocaster. |
+MEDIA (media) | Titles of films, books, magazines, songs and albums, fictional characters and languages. | Forbes, American Psycho, Kiss Me Once, Twin Peaks, Disney Adventures. |
+PLANT (plant) | Types of trees, flowers, and other plants, including their scientific names. | Salix, Quercus Petraea, Douglas Fir, Forsythia, Artemisia Maritima. |
+MYTH (mythological) | Mythological and religious entities. | Apollo, Persephone, Aphrodite, Saint Peter, Pope Gregory I, Hercules. |
+TIME (time) | Specific and well-defined time intervals, such as eras, historical periods, centuries, years and important days. No months and days of the week. | Renaissance, Middle Ages, Christmas, Great Depression, 17th Century, 2012. |
+VEHI (vehicle) | Cars, motorcycles and other vehicles. | Ferrari Testarossa, Suzuki Jimny, Honda CR-X, Boeing 747, Fairey Fulmar.
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 Only trained on English split of MultiNERD dataset. Therefore will not perform well on other languages.
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use the code below to get started with the model:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```py
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 
-## How to Get Started with the Model
+tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
 
-Use the code below to get started with the model.
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin"
 
-[More Information Needed]
+ner_results = nlp(example)
+print(ner_results)
+```
 
 ## 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]
+50% of train split of MultiNERD dataset was used to finetune the model.
 
 ### Training Procedure 
 
-<!-- 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]
+English dataset was filterd out : `train_dataset = train_dataset.filter(lambda x: x['lang'] == 'en')`
 
 
 #### 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]
+The following hyperparameters were used during training:
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+learning_rate: 5e-05
+train_batch_size: 32
+eval_batch_size: 32
+seed: 42
+optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+lr_scheduler_type: linear
+lr_scheduler_warmup_ratio: 0.1
+num_epochs: 1
 
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Evaluation was perfored on 50% of evaluation split of MultiNERD dataset.
 
 ### Testing Data & Metrics
 
-#### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+#### Testing Data
 
-[More Information Needed]
+Tested on Full test split of MultiNERD dataset.
 
 
 #### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Model versions and checkpoint were evaluated using F1, Precision and Recall.
+For this `seqeval` metric was used: `metric = load_metric("seqeval")`.
 
 ### Results
 
-[More Information Needed]
-
+|Entity | Precision | Recall | F1 score | Support |
+|---|---|---|---|---|
+|ANIM | 0.71 | 0.77 | 0.739 | 1604 |
+|BIO | 0.5 | 0.125 | 0.2 | 8 |
+|CEL | 0.738 | 0.756 | 0.746 | 41 |
+|DIS | 0.737 | 0.772 | 0.754 | 759 |
+|EVE | 0.952 | 0.968 | 0.960 | 352 |
+|FOOD | 0.679 | 0.545 | 0.605 | 566 |
+|INST | 0.75 | 0.75 | 0.75 | 12 |
+|LOC | 0.994 | 0.991 | 0.993 | 12024 |
+|MEDIA | 0.940 | 0.969 | 0.954 | 458 |
+|ORG | 0.977 | 0.981 | 0.979 | 3309 |
+|PER | 0.992 | 0.995 | 0.993 | 5265 |
+|PLANT | 0.617 | 0.730 | 0.669 | 894 |
+|MYTH | 0.647 | 0.687 | 0.666 | 32 |
+|TIME | 0.825 | 0.820 | 0.822 | 289 |
+|VEHI | 0.812 | 0.812 | 0.812 | 32 |
+|**Overall** | **0.939** | **0.947** | **0.943** |
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 Follows the same as RoBERTa-BASE
-[More Information Needed]
 
 ### Compute Infrastructure
-2x T4 GPUs
-[More Information Needed]
 
 #### Hardware
 
-[More Information Needed]
+Kaggle - GPU T4x2
+Google Colab - GPU T4x1
 
 #### Software
-Pytorch
-[More Information Needed]
-
+pandas==1.5.3
+numpy==1.23.5
+seqeval==1.2.2
+datasets==2.15.0
+huggingface_hub==0.19.4
+transformers[torch]==4.35.2
+evaluate==0.4.1
+matplotlib==3.7.1
+collections
+torch==2.0.0
 
 ## Model Card Contact
-(jayant-yadav)[https://huggingface.co/jayant-yadav]
-[More Information Needed]
+[jayant-yadav](https://huggingface.co/jayant-yadav)