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README.md

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 ---
-license: osl-3.0
-datasets:
-- SandeepKanao/HL7-FHIR-Synthetic-Dataset
+license: apache-2.0
 language:
 - en
-metrics:
-- bertscore
-- accuracy
-- character
-library_name: bertopic
 tags:
-- medical
-pipeline_tag: text-classification
+- Token Classification
+co2_eq_emissions: 0.0279399890043426
+widget:
+- text: "CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations.
+The symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea.
+Except for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at the left sternal border with inspiratory accentuation), physical examination yielded unremarkable findings."
+  example_title: "example 1"
+- text: "A 63-year-old woman with no known cardiac history presented with a sudden onset of dyspnea requiring intubation and ventilatory support out of hospital.
+She denied preceding symptoms of chest discomfort, palpitations, syncope or infection.
+The patient was afebrile and normotensive, with a sinus tachycardia of 140 beats/min."
+  example_title: "example 2"
+- text: "A 48 year-old female presented with vaginal bleeding and abnormal Pap smears.
+Upon diagnosis of invasive non-keratinizing SCC of the cervix, she underwent a radical hysterectomy with salpingo-oophorectomy which demonstrated positive spread to the pelvic lymph nodes and the parametrium.
+Pathological examination revealed that the tumour also extensively involved the lower uterine segment."
+  example_title: "example 3"
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+## About the Model
+An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
 
-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).
+- Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
+- Carbon emission: 0.0279399890043426 Kg
+- Training time: 30.16527 minutes
+- GPU used : 1 x GeForce RTX 3060 Laptop GPU
 
-## Model Details
+Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18
 
-### Model Description
+## Usage
+The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
+```python
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForTokenClassification
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [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]
-
-<!-- Provide the basic links for the model. -->
-
-- **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
-
-<!-- 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]
-
-### Out-of-Scope Use
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Data 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 
-
-<!-- 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 Data 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
-
-<!-- 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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
+model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
 
+pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
+pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")
+```
 
+## Author
+This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
+> https://github.com/dreji18/Bio-Epidemiology-NER

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "O",
+    "1": "B-Activity",
+    "2": "B-Administration",
+    "3": "B-Age",
+    "4": "B-Area",
+    "5": "B-Biological_attribute",
+    "6": "B-Biological_structure",
+    "7": "B-Clinical_event",
+    "8": "B-Color",
+    "9": "B-Coreference",
+    "10": "B-Date",
+    "11": "B-Detailed_description",
+    "12": "B-Diagnostic_procedure",
+    "13": "B-Disease_disorder",
+    "14": "B-Distance",
+    "15": "B-Dosage",
+    "16": "B-Duration",
+    "17": "B-Family_history",
+    "18": "B-Frequency",
+    "19": "B-Height",
+    "20": "B-History",
+    "21": "B-Lab_value",
+    "22": "B-Mass",
+    "23": "B-Medication",
+    "24": "B-Non[biological](Detailed_description",
+    "25": "B-Nonbiological_location",
+    "26": "B-Occupation",
+    "27": "B-Other_entity",
+    "28": "B-Other_event",
+    "29": "B-Outcome",
+    "30": "B-Personal_[back](Biological_structure",
+    "31": "B-Personal_background",
+    "32": "B-Qualitative_concept",
+    "33": "B-Quantitative_concept",
+    "34": "B-Severity",
+    "35": "B-Sex",
+    "36": "B-Shape",
+    "37": "B-Sign_symptom",
+    "38": "B-Subject",
+    "39": "B-Texture",
+    "40": "B-Therapeutic_procedure",
+    "41": "B-Time",
+    "42": "B-Volume",
+    "43": "B-Weight",
+    "44": "I-Activity",
+    "45": "I-Administration",
+    "46": "I-Age",
+    "47": "I-Area",
+    "48": "I-Biological_attribute",
+    "49": "I-Biological_structure",
+    "50": "I-Clinical_event",
+    "51": "I-Color",
+    "52": "I-Coreference",
+    "53": "I-Date",
+    "54": "I-Detailed_description",
+    "55": "I-Diagnostic_procedure",
+    "56": "I-Disease_disorder",
+    "57": "I-Distance",
+    "58": "I-Dosage",
+    "59": "I-Duration",
+    "60": "I-Family_history",
+    "61": "I-Frequency",
+    "62": "I-Height",
+    "63": "I-History",
+    "64": "I-Lab_value",
+    "65": "I-Mass",
+    "66": "I-Medication",
+    "67": "I-Nonbiological_location",
+    "68": "I-Occupation",
+    "69": "I-Other_entity",
+    "70": "I-Other_event",
+    "71": "I-Outcome",
+    "72": "I-Personal_background",
+    "73": "I-Qualitative_concept",
+    "74": "I-Quantitative_concept",
+    "75": "I-Severity",
+    "76": "I-Shape",
+    "77": "I-Sign_symptom",
+    "78": "I-Subject",
+    "79": "I-Texture",
+    "80": "I-Therapeutic_procedure",
+    "81": "I-Time",
+    "82": "I-Volume",
+    "83": "I-Weight"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "B-Activity": 1,
+    "B-Administration": 2,
+    "B-Age": 3,
+    "B-Area": 4,
+    "B-Biological_attribute": 5,
+    "B-Biological_structure": 6,
+    "B-Clinical_event": 7,
+    "B-Color": 8,
+    "B-Coreference": 9,
+    "B-Date": 10,
+    "B-Detailed_description": 11,
+    "B-Diagnostic_procedure": 12,
+    "B-Disease_disorder": 13,
+    "B-Distance": 14,
+    "B-Dosage": 15,
+    "B-Duration": 16,
+    "B-Family_history": 17,
+    "B-Frequency": 18,
+    "B-Height": 19,
+    "B-History": 20,
+    "B-Lab_value": 21,
+    "B-Mass": 22,
+    "B-Medication": 23,
+    "B-Non[biological](Detailed_description": 24,
+    "B-Nonbiological_location": 25,
+    "B-Occupation": 26,
+    "B-Other_entity": 27,
+    "B-Other_event": 28,
+    "B-Outcome": 29,
+    "B-Personal_[back](Biological_structure": 30,
+    "B-Personal_background": 31,
+    "B-Qualitative_concept": 32,
+    "B-Quantitative_concept": 33,
+    "B-Severity": 34,
+    "B-Sex": 35,
+    "B-Shape": 36,
+    "B-Sign_symptom": 37,
+    "B-Subject": 38,
+    "B-Texture": 39,
+    "B-Therapeutic_procedure": 40,
+    "B-Time": 41,
+    "B-Volume": 42,
+    "B-Weight": 43,
+    "I-Activity": 44,
+    "I-Administration": 45,
+    "I-Age": 46,
+    "I-Area": 47,
+    "I-Biological_attribute": 48,
+    "I-Biological_structure": 49,
+    "I-Clinical_event": 50,
+    "I-Color": 51,
+    "I-Coreference": 52,
+    "I-Date": 53,
+    "I-Detailed_description": 54,
+    "I-Diagnostic_procedure": 55,
+    "I-Disease_disorder": 56,
+    "I-Distance": 57,
+    "I-Dosage": 58,
+    "I-Duration": 59,
+    "I-Family_history": 60,
+    "I-Frequency": 61,
+    "I-Height": 62,
+    "I-History": 63,
+    "I-Lab_value": 64,
+    "I-Mass": 65,
+    "I-Medication": 66,
+    "I-Nonbiological_location": 67,
+    "I-Occupation": 68,
+    "I-Other_entity": 69,
+    "I-Other_event": 70,
+    "I-Outcome": 71,
+    "I-Personal_background": 72,
+    "I-Qualitative_concept": 73,
+    "I-Quantitative_concept": 74,
+    "I-Severity": 75,
+    "I-Shape": 76,
+    "I-Sign_symptom": 77,
+    "I-Subject": 78,
+    "I-Texture": 79,
+    "I-Therapeutic_procedure": 80,
+    "I-Time": 81,
+    "I-Volume": 82,
+    "I-Weight": 83,
+    "O": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.1",
+  "vocab_size": 30522
+}

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pytorch_model.bin

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "cuda": 0,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "distilbert-base-uncased",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}