
# 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.

_(Please note that ManaGPT-1020 has superseded **[ManaGPT-1010](https://huggingface.co/NeuraXenetica/ManaGPT-1010)**: the newer model has been fine-tuned on a dataset roughly 6.45 times the size of that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

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

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 and hyperparameters

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

### Framework versions

- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["en"], "license": "mit", "tags": ["management", "text generation"], "pipeline_tag": "text-generation", "widget": [{"text": "Working for a robotic boss would be like", "example_title": "Robotic boss"}, {"text": "The workplace of tomorrow will", "example_title": "Workplace of tomorrow"}, {"text": "Working alongside an artificially intelligent coworker would be like", "example_title": "AI coworker"}, {"text": "The hybrid human-robotic organization of the future will", "example_title": "Hybrid organization"}, {"text": "Information security within future organizations will be difficult to enforce, because", "example_title": "InfoSec of the future"}, {"text": "It will be difficult for robotic employees to", "example_title": "Robots' difficulties"}, {"text": "In the future, human workers will", "example_title": "Human workers"}, {"text": "Synthetic organizations will", "example_title": "Synthetic organizations"}, {"text": "Technological posthumanization is", "example_title": "Posthumanization"}, {"text": "Social robots are", "example_title": "Social robots"}, {"text": "Society 5.0 is", "example_title": "Society 5.0"}, {"text": "For most organizations, artificial general intelligence will", "example_title": "AGI"}], "model-index": [{"name": "ManaGPT-1010", "results": []}]}
transformers tf gpt2 text-generation management text generation en license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #tf #gpt2 #text-generation #management #text generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# ManaGPT-1020
<img style="float:right; margin:10px; margin-right:30px" src="URL 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.

_(Please note that ManaGPT-1020 has superseded 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

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

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 and hyperparameters

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

### Framework versions

- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
# ManaGPT-1020
<img style="float:right; margin:10px; margin-right:30px" src="URL 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.

_(Please note that ManaGPT-1020 has superseded 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

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

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 and hyperparameters

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 ### Framework versions

- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #tf #gpt2 #text-generation #management #text generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # ManaGPT-1020
<img style="float:right; margin:10px; margin-right:30px" src="URL 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.

_(Please note that ManaGPT-1020 has superseded 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

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

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 and hyperparameters

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 ### Framework versions

- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #tf #gpt2 #text-generation #management #text generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# ManaGPT-1020
<img style="float:right; margin:10px; margin-right:30px" src="URL 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.

_(Please note that ManaGPT-1020 has superseded 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

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

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 and hyperparameters

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### Framework versions

- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2