# Model Card for Model ID


test

---
co2_eq_emissions:
  emissions: number (in grams of CO2)
  source: "source of the information, either directly from AutoTrain, code carbon or from a scientific article documenting the model"
  training_type: "pre-training or fine-tuning"
  geographical_location: "as granular as possible, for instance Quebec, Canada or Brooklyn, NY, USA. To check your compute's electricity grid, you can check out https://app.electricitymap.org."
  hardware_used: "how much compute and what kind, e.g. 8 v100 GPUs"
---
{"language": ["en"], "license": "apache-2.0", "library_name": "pytorch", "tags": ["biology", "medical"], "metrics": ["roc_auc"], "model_type": "pytorch", "framework": "pt", "widget": [{"text": "Is this review positive or negative? Review: Best cast iron skillet you will ever buy.", "example_title": "Sentiment analysis"}]}
pytorch bert biology medical en license:apache-2.0 region:us
TAGS
#pytorch #bert #biology #medical #en #license-apache-2.0 #region-us 

# Model Card for Model ID


test

---
co2_eq_emissions:
  emissions: number (in grams of CO2)
  source: "source of the information, either directly from AutoTrain, code carbon or from a scientific article documenting the model"
  training_type: "pre-training or fine-tuning"
  geographical_location: "as granular as possible, for instance Quebec, Canada or Brooklyn, NY, USA. To check your compute's electricity grid, you can check out URL."
  hardware_used: "how much compute and what kind, e.g. 8 v100 GPUs"
---
# Model Card for Model ID


test

---
co2_eq_emissions:
  emissions: number (in grams of CO2)
  source: "source of the information, either directly from AutoTrain, code carbon or from a scientific article documenting the model"
  training_type: "pre-training or fine-tuning"
  geographical_location: "as granular as possible, for instance Quebec, Canada or Brooklyn, NY, USA. To check your compute's electricity grid, you can check out URL."
  hardware_used: "how much compute and what kind, e.g. 8 v100 GPUs"
---
TAGS
#pytorch #bert #biology #medical #en #license-apache-2.0 #region-us 
 # Model Card for Model ID


test

---
co2_eq_emissions:
  emissions: number (in grams of CO2)
  source: "source of the information, either directly from AutoTrain, code carbon or from a scientific article documenting the model"
  training_type: "pre-training or fine-tuning"
  geographical_location: "as granular as possible, for instance Quebec, Canada or Brooklyn, NY, USA. To check your compute's electricity grid, you can check out URL."
  hardware_used: "how much compute and what kind, e.g. 8 v100 GPUs"
---
TAGS
#pytorch #bert #biology #medical #en #license-apache-2.0 #region-us 
# Model Card for Model ID


test

---
co2_eq_emissions:
  emissions: number (in grams of CO2)
  source: "source of the information, either directly from AutoTrain, code carbon or from a scientific article documenting the model"
  training_type: "pre-training or fine-tuning"
  geographical_location: "as granular as possible, for instance Quebec, Canada or Brooklyn, NY, USA. To check your compute's electricity grid, you can check out URL."
  hardware_used: "how much compute and what kind, e.g. 8 v100 GPUs"
---