Model save

README.md

@@ -1,207 +1,32 @@
 ---
-language:
-- en
-license: apache-2.0
-library_name: transformers
+base_model: dmis-lab/biobert-base-cased-v1.2
 tags:
-- medical
-- pharmacovigilance
-- vaccines
-datasets:
-- chrisvoncsefalvay/vaers-outcomes
-metrics:
-- accuracy
-- f1
-- precision
-- recall
-dataset: chrisvoncsefalvay/vaers-outcomes
-pipeline_tag: text-classification
-widget:
-- text: Patient is a 90 y.o. male with a PMH of IPF, HFpEF, AFib (Eliquis), Metastatic
-    Prostate Cancer who presented to Hospital 10/28/2023 following an unwitnessed
-    fall at his assisted living. He was found to have an AKI, pericardial effusion,
-    hypoxia, AMS, and COVID-19. His hospital course was complicated by delirium and
-    aspiration, leading to acute hypoxic respiratory failure requiring BiPAP and transfer
-    to the ICU. Palliative Care had been following, and after goals of care conversations
-    on 11/10/2023 the patient was transitioned to DNR-CC. Patient expired at 0107
-    11/12/23.
-  example_title: VAERS 2727645 (hospitalisation, death)
-- text: 'hospitalized for paralytic ileus a week after the vaccination; This serious
-    case was reported by a physician via call center representative and described
-    the occurrence of ileus paralytic in a patient who received Rota (Rotarix liquid
-    formulation) for prophylaxis. On an unknown date, the patient received the 1st
-    dose of Rotarix liquid formulation. On an unknown date, less than 2 weeks after
-    receiving Rotarix liquid formulation, the patient experienced ileus paralytic
-    (Verbatim: hospitalized for paralytic ileus a week after the vaccination) (serious
-    criteria hospitalization and GSK medically significant). The outcome of the ileus
-    paralytic was not reported. It was unknown if the reporter considered the ileus
-    paralytic to be related to Rotarix liquid formulation. It was unknown if the company
-    considered the ileus paralytic to be related to Rotarix liquid formulation. Additional
-    Information: GSK Receipt Date: 27-DEC-2023 Age at vaccination and lot number were
-    not reported. The patient of unknown age and gender was hospitalized for paralytic
-    ileus a week after the vaccination. The reporting physician was in charge of the
-    patient.'
-  example_title: VAERS 2728408 (hospitalisation)
-- text: Patient received Pfizer vaccine 7 days beyond BUD. According to Pfizer manufacturer
-    research data, vaccine is stable and effective up to 2 days after BUD. Waiting
-    for more stability data from PFIZER to determine if revaccination is necessary.
-  example_title: VAERS 2728394 (no event)
-- text: Fever of 106F rectally beginning 1 hr after immunizations and lasting <24
-    hrs. Seen at ER treated w/tylenol & cool baths.
-  example_title: VAERS 25042 (ER attendance)
-- text: I had the MMR shot last week, and I felt a little dizzy afterwards, but it
-    passed after a few minutes and I'm doing fine now.
-  example_title: 'Non-sample example: simulated informal patient narrative (no event)'
-- text: My niece had the COVID vaccine. A few weeks later, she was T-boned by a drunk
-    driver. She called me from the ER. She's fully recovered now, though.
-  example_title: 'Non-sample example: simulated informal patient narrative (ER attendance,
-    albeit unconnected)'
+- generated_from_trainer
 model-index:
 - name: daedra
-  results:
-  - task:
-      type: text-classification
-    dataset:
-      name: vaers-outcomes
-      type: vaers-outcomes
-    metrics:
-    - type: accuracy_microaverage
-      value: 0.885
-      name: Accuracy, microaveraged
-      verified: false
-    - type: f1_microaverage
-      value: 0.885
-      name: F1 score, microaveraged
-      verified: false
-    - type: precision_macroaverage
-      value: 0.769
-      name: Precision, macroaveraged
-      verified: false
-    - type: recall_macroaverage
-      value: 0.688
-      name: Recall, macroaveraged
-      verified: false
+  results: []
 ---
 
-# DAEDRA: Determining Adverse Event Disposition for Regulatory Affairs
+<!-- 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. -->
 
-This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) trained on the [VAERS adversome outcomes data set](https://huggingface.com/datasets/chrisvoncsefalvay/vaers-outcomes). 
+# daedra
 
-#  Table of Contents
+This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on an unknown dataset.
 
-- [Model Details](#model-details)
-- [Uses](#uses)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-- [Training Details](#training-details)
-- [Evaluation](#evaluation)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications](#technical-specifications-optional)
-- [Citation](#citation)
+## Model description
 
+More information needed
 
-# Model Details
+## Intended uses & limitations
 
-## Model Description
+More information needed
 
-<!-- Provide a longer summary of what this model is/does. -->
+## Training and evaluation data
 
-DAEDRA is a model for the identification of adverse event dispositions (outcomes) from passive pharmacovigilance data.
-The model is trained on a real-world adversomics data set spanning over three decades (1990-2023) and comprising over 1.8m records for a total corpus of 173,093,850 words constructed from a subset of reports submitted to VAERS.
-It is intended to identify, based on the narrative, whether any, or any combination, of three serious outcomes -- death, hospitalisation and ER attendance -- have occurred. 
+More information needed
 
-
-- **Developed by:** Chris von Csefalvay
-- **Model type:** Language model
-- **Language(s) (NLP):** en
-- **License:** apache-2.0
-- **Parent Model:** [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2)
-- **Resources for more information:** 
-    - [GitHub Repo](https://github.com/chrisvoncsefalvay/daedra)
-
-
-# Uses
-
-This model was designed to facilitate the coding of passive adverse event reports into severity outcome categories.
-
-## Direct Use
-
-Load the model via the `transformers` library:
-
-```
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("chrisvoncsefalvay/daedra")
-model = AutoModel.from_pretrained("chrisvoncsefalvay/daedra")
-```
-
-## Out-of-Scope Use
-
-This model is not intended for the diagnosis or treatment of any disease. 
-
-
-# Bias, Risks, and Limitations
-
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
-
-
-
-# Training Details
-
-## Training Data
-
-The model was trained on the [VAERS adversome outcomes data set](https://huggingface.com/datasets/chrisvoncsefalvay/vaers-outcomes), which comprises 1,814,920 reports from the FDA's Vaccine Adverse Events Reporting System (VAERS). Reports were split into a 70% training set and a 15% test set and 15% validation set after age and gender matching.
-
-## Training Procedure
-
-Training was conducted on an Azure `Standard_NC24s_v3` instance in `us-east`, with 4x Tesla V100-PCIE-16GB GPUs and 24x Intel Xeon E5-2690 v4 CPUs at 2.60GHz.
-
-### Speeds, Sizes, Times
-
-Training took 15 hours and 10 minutes.
- 
-
-## Testing Data, Factors & Metrics
-
-### Testing Data
-
-The model was tested on the `test` partition of the [VAERS adversome outcomes data set](https://huggingface.com/datasets/chrisvoncsefalvay/vaers-outcomes).
-
-## Results 
-
-On the test set, the model achieved the following results:
-
-* `f1`: 0.885
-* `precision` and `recall`, microaveraged: 0.885
-* `precision`, macroaveraged: 0.769
-* `recall`, macroaveraged: 0.688
-
-
-# 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:** 4 x Tesla V100-PCIE-16GB
-- **Hours used:** 15.166
-- **Cloud Provider:** Azure
-- **Compute Region:** us-east
-- **Carbon Emitted:** 6.72 kg CO2eq (offset by provider)
-
-
-# Citation
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-Forthcoming -- watch this space.
-
-# Model Card Authors
-
-<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
-
-Chris von Csefalvay
+## Training procedure
 
 ### Training hyperparameters
 
@@ -212,7 +37,7 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 5
 
 ### Framework versions
 

model.safetensors

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