Update README.md

README.md

@@ -1,48 +1,59 @@
 ---
 license: mit
 tags:
-- generated_from_keras_callback
+- management
+- text generation
 model-index:
-- name: ManaGPT-1020
+- name: ManaGPT-1010
   results: []
+language:
+- en
+pipeline_tag: text-generation
+
+widget:
+- text: "Within a cyber-physical system, social robots should be expected to "
+  example_title: "Social robots"
+- text: "Neuroprosthetic implants in the workplace"
+  example_title: "Neuroprosthetic implants"
+- text: "It will be difficult for robotic employees to"
+  example_title: "Robotic employees' difficulties"
+- text: "Artificial agents for business"
+  example_title: "Artificial agents for business"
+- text: "The strategic use of robots"
+  example_title: "The strategic use of robots"
+- text: "Information security within future organizations will be difficult to enforce, because"
+  example_title: "InfoSec challenges"
+- text: "Artificial intelligence within businesses"
+  example_title: "AI within businesses"
+- text: "Tomorrow's robots will"
+  example_title: ""
+- text: "For most organizations, artificial general intelligence will"
+  example_title: "AGI for organizations"  
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
 # ManaGPT-1020
+<img style="float:right; margin:10px; margin-right:30px" src="https://huggingface.co/NeuraXenetica/ManaGPT-1010/resolve/main/ManaGPT_logo_01.png" width="150" height="150"></img>
+**ManaGPT-1020** is an experimental open-source text-generating AI designed to offer insights on the role of emerging technologies in organizational management.
 
-This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-
+_(Please note that ManaGPT-1020 has superseded **[ManaGPT-1010](https://huggingface.co/NeuraXenetica/ManaGPT-1010)**: the newer model has been fine-tuned on a dataset roughly 6.45 times the size of that used to fine-tune ManaGPT-1010.)_
 
 ## Model description
 
-More information needed
+The model is a fine-tuned version of GPT-2 that has been trained on a custom corpus of scholarly and popular texts from the field of organizational management that relate to ongoing effects of posthumanizing technologies (e.g., relating to advanced artificial intelligence, social robotics, virtual reality, neuroprosthetics, and cyber-physical systems) on the structure of organizations and human beings’ experience of organizational life.
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
+This model has been designed for experimental research purposes; it isn’t intended for use in a production setting or in any sensitive or potentially hazardous contexts.
 
-## Training procedure
+## Training procedure and hyperparameters
 
-### Training hyperparameters
-
-The following hyperparameters were used during training:
+The model was trained using a Tesla T4 with 16GB of GPU memory. The following hyperparameters were used during training:
 - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
 - training_precision: float32
 
-### Training results
-
-
-
 ### Framework versions
 
-- Transformers 4.27.2
+- Transformers 4.27.1
 - TensorFlow 2.11.0
 - Datasets 2.10.1
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2