Update README.md

README.md

@@ -1,8 +1,70 @@
 ---
 license: mit
-language:
-- en
-pipeline_tag: token-classification
+base_model: microsoft/deberta-v3-base
 tags:
-- medical
----
+- generated_from_trainer
+model-index:
+- name: deberta-med-ner-2
+  results: []
+
+widget:
+- 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 1"
+
+---
+
+<!-- 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. -->
+
+
+
+
+# deberta-med-ner-2
+
+This model is a fine-tuned version of [DeBERTaV3](https://huggingface.co/microsoft/deberta-v3-base) on the PubMED Dataset.
+
+## Model description
+
+MED-NER Model was finetuned on BERT to recognize 41 Medical entities.
+
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 32
+- seed: 69
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 25
+- mixed_precision_training: Native AMP
+
+
+
+## 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
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+pipe = pipeline("token-classification", model="NeuronZero/MED-NER", aggregation_strategy='simple')
+
+result = pipe('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.')
+
+
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+tokenizer = AutoTokenizer.from_pretrained("NeuronZero/MED-NER")
+model = AutoModelForTokenClassification.from_pretrained("NeuronZero/MED-NER")
+```
+