# MechDistilGPT2
## Table of Contents
- [Model Details](#model-details) 
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Environmental Impact](#environmental-impact)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)

## Model Details
- **Model Description:** 
This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.


- **Developed by:** [Ashwin](https://huggingface.co/geralt)

- **Model Type:**  Causal Language modeling
- **Language(s):** English
- **License:** [More Information Needed]
- **Parent Model:** See the [DistilGPT2model](https://huggingface.co/distilgpt2) for more information about the Distilled-GPT2 base model.
- **Resources for more information:**
  - [Research Paper](https://arxiv.org/abs/2105.09680)
  - [GitHub Repo](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb)

## Uses

#### Direct Use

The model can be used for tasks including topic classification, Causal Language modeling and text generation


#### Misuse and Out-of-scope Use

The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.


## Risks, Limitations and Biases

**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**

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)).


## Training

#### Training Data

This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.


#### Training Procedure

###### Fine-Tuning

* Default Training Args
* Epochs = 3
* Training set = 200k sentences
* Validation set = 40k sentences

###### Framework versions

* Transformers 4.7.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.10.2


# Environmental Impact

​
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:** [More information needed]
- **Hours used:** [More information needed]
- **Cloud Provider:** [More information needed]
- **Compute Region:** [More information needed"]
- **Carbon Emitted:** [More information needed]
​

## How to Get Started With the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("geralt/MechDistilGPT2")

model = AutoModelForCausalLM.from_pretrained("geralt/MechDistilGPT2")

```

{"tags": ["Causal Language modeling", "text-generation", "CLM"], "model_index": [{"name": "MechDistilGPT2", "results": [{"task": {"name": "Causal Language modeling", "type": "Causal Language modeling"}}]}]}
transformers pytorch gpt2 text-generation Causal Language modeling CLM arxiv:2105.09680 arxiv:1910.09700 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #Causal Language modeling #CLM #arxiv-2105.09680 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# MechDistilGPT2
## Table of Contents
- Model Details 
- Uses
- Risks, Limitations and Biases
- Training
- Environmental Impact
- How to Get Started With the Model

## Model Details
- Model Description: 
This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.


- Developed by: Ashwin

- Model Type:  Causal Language modeling
- Language(s): English
- License: 
- Parent Model: See the DistilGPT2model for more information about the Distilled-GPT2 base model.
- Resources for more information:
  - Research Paper
  - GitHub Repo

## Uses

#### Direct Use

The model can be used for tasks including topic classification, Causal Language modeling and text generation


#### Misuse and Out-of-scope Use

The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.


## Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).


## Training

#### Training Data

This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.


#### Training Procedure

###### Fine-Tuning

* Default Training Args
* Epochs = 3
* Training set = 200k sentences
* Validation set = 40k sentences

###### Framework versions

* Transformers 4.7.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.10.2


# Environmental Impact

​
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
​
- Hardware Type: [More information needed]
- Hours used: [More information needed]
- Cloud Provider: [More information needed]
- Compute Region: [More information needed"]
- Carbon Emitted: [More information needed]
​

## How to Get Started With the Model



# MechDistilGPT2 ## Table of Contents
- Model Details 
- Uses
- Risks, Limitations and Biases
- Training
- Environmental Impact
- How to Get Started With the Model ## Model Details
- Model Description: 
This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.


- Developed by: Ashwin

- Model Type:  Causal Language modeling
- Language(s): English
- License: 
- Parent Model: See the DistilGPT2model for more information about the Distilled-GPT2 base model.
- Resources for more information:
  - Research Paper
  - GitHub Repo ## Uses #### Direct Use

The model can be used for tasks including topic classification, Causal Language modeling and text generation #### Misuse and Out-of-scope Use

The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). ## Training #### Training Data

This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books. #### Training Procedure ###### Fine-Tuning

* Default Training Args
* Epochs = 3
* Training set = 200k sentences
* Validation set = 40k sentences ###### Framework versions

* Transformers 4.7.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.10.2 # Environmental Impact

​
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
​
- Hardware Type: [More information needed]
- Hours used: [More information needed]
- Cloud Provider: [More information needed]
- Compute Region: [More information needed"]
- Carbon Emitted: [More information needed]
​ ## How to Get Started With the Model
TAGS
#transformers #pytorch #gpt2 #text-generation #Causal Language modeling #CLM #arxiv-2105.09680 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # MechDistilGPT2 ## Table of Contents
- Model Details 
- Uses
- Risks, Limitations and Biases
- Training
- Environmental Impact
- How to Get Started With the Model ## Model Details
- Model Description: 
This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.


- Developed by: Ashwin

- Model Type:  Causal Language modeling
- Language(s): English
- License: 
- Parent Model: See the DistilGPT2model for more information about the Distilled-GPT2 base model.
- Resources for more information:
  - Research Paper
  - GitHub Repo ## Uses #### Direct Use

The model can be used for tasks including topic classification, Causal Language modeling and text generation #### Misuse and Out-of-scope Use

The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). ## Training #### Training Data

This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books. #### Training Procedure ###### Fine-Tuning

* Default Training Args
* Epochs = 3
* Training set = 200k sentences
* Validation set = 40k sentences ###### Framework versions

* Transformers 4.7.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.10.2 # Environmental Impact

​
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
​
- Hardware Type: [More information needed]
- Hours used: [More information needed]
- Cloud Provider: [More information needed]
- Compute Region: [More information needed"]
- Carbon Emitted: [More information needed]
​ ## How to Get Started With the Model
passage: TAGS
#transformers #pytorch #gpt2 #text-generation #Causal Language modeling #CLM #arxiv-2105.09680 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# MechDistilGPT2## Table of Contents
- Model Details 
- Uses
- Risks, Limitations and Biases
- Training
- Environmental Impact
- How to Get Started With the Model## Model Details
- Model Description: 
This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.


- Developed by: Ashwin

- Model Type:  Causal Language modeling
- Language(s): English
- License: 
- Parent Model: See the DistilGPT2model for more information about the Distilled-GPT2 base model.
- Resources for more information:
  - Research Paper
  - GitHub Repo## Uses#### Direct Use

The model can be used for tasks including topic classification, Causal Language modeling and text generation#### Misuse and Out-of-scope Use

The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.## Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).## Training#### Training Data

This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.#### Training Procedure###### Fine-Tuning

* Default Training Args
* Epochs = 3
* Training set = 200k sentences
* Validation set = 40k sentences