Next word generator trained on questions. Receives partial questions and tries to predict the next word.
Example use:

```python
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer

model_name = "allenai/t5-small-squad2-next-word-generator-squad"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

def run_model(input_string, **generator_args):
    input_ids = tokenizer.encode(input_string, return_tensors="pt")
    res = model.generate(input_ids, **generator_args)
    output = tokenizer.batch_decode(res, skip_special_tokens=True)
    print(output)
    return output

run_model("Which")
run_model("Which two")
run_model("Which two counties")
run_model("Which two counties are")
run_model("Which two counties are the")
run_model("Which two counties are the biggest")
run_model("Which two counties are the biggest economic")
run_model("Which two counties are the biggest economic powers")

```
which should result in the following:
```
['one']
['statements']
['are']
['in']
['most']
['in']
['zones']
['of']
```


---
language: en
widget:
- text: $answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?
license: apache-2.0
---

# macaw-large

## Model description

Macaw (Multi-angle c(q)uestion answering) is a ready-to-use model capable of
general question answering,
showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion,
which means it can handle a flexible set of input and output "slots"
(question, answer, multiple-choice options, context, and explanation) .

Macaw was built on top of [T5](https://github.com/google-research/text-to-text-transfer-transformer) and comes in
three sizes: [macaw-11b](https://huggingface.co/allenai/macaw-11b), [macaw-3b](https://huggingface.co/allenai/macaw-3b),
and [macaw-large](https://huggingface.co/allenai/macaw-large), as well as an answer-focused version featured on
various leaderboards [macaw-answer-11b](https://huggingface.co/allenai/macaw-answer-11b).

See https://github.com/allenai/macaw for more details.


---
tags:
- conversational
---

# Issei DialoGPT Model


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.923
    - name: F1
      type: f1
      value: 0.9233262687967644
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2180
- Accuracy: 0.923
- F1: 0.9233

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8217        | 1.0   | 250  | 0.3137          | 0.903    | 0.8999 |
| 0.2484        | 2.0   | 500  | 0.2180          | 0.923    | 0.9233 |

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


---
language: si
tags:
- Sinhala
- text-generation
- gpt2
datasets:
- mc4
---
### Overview

This is a smaller GPT2 model trained on [MC4](https://github.com/allenai/allennlp/discussions/5056) Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.

## Model Specification

The model chosen for training is GPT2 with the following specifications:
 1. vocab_size=50257
 2. n_embd=768
 3. n_head=12
 4. n_layer=12
 5. n_positions=1024

## How to Use
You can use this model directly with a pipeline for casual language modeling:

```py
from transformers import pipeline
generator = pipeline('text-generation', model='keshan/sinhala-gpt2')
generator("මම", max_length=50, num_return_sequences=5)
```


---
language: si
tags:
- oscar
- Sinhala
- roberta
- fill-mask
widget:
- text: "මම සිංහල භාෂාව <mask>"
datasets:
- oscar
---
### Overview

This is a slightly smaller model trained on [OSCAR](https://oscar-corpus.com/) Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.

## Model Specification

The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications:
 1. vocab_size=50265
 2. max_position_embeddings=514
 3. num_attention_heads=12
 4. num_hidden_layers=12
 5. type_vocab_size=1

## How to Use
You can use this model directly with a pipeline for masked language modeling:

```py
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline

model = AutoModelWithLMHead.from_pretrained("keshan/sinhala-roberta-oscar")
tokenizer = AutoTokenizer.from_pretrained("keshan/sinhala-roberta-oscar")

fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

fill_mask("මම ගෙදර <mask>.")

```


---
language: si
tags:
- sinhala
- gpt2
pipeline_tag: text-generation
widget:
- text: "මම"
---
This is a finetunes version of keshan/sinhala-gpt2 with newswire articles. This was finetuned on ~12MB of data
- Num examples=8395
- Batch size =8

It got a Perplexity of 3.15


---
language: si
license: cc-by-4.0
tags:
- sinhala
- roberta
pipeline_tag: fill-mask
widget:
- text: මම සිංහල භාෂාව <mask>
---
# Sinhala roberta on mc4 dataset


---
language: si
tags:
- SinhalaBERTo
- Sinhala
- roberta
datasets:
- oscar
---
### Overview

This is a slightly smaller model trained on [OSCAR](https://oscar-corpus.com/) Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.

## Model Specification

The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications:
 1. vocab_size=52000
 2. max_position_embeddings=514
 3. num_attention_heads=12
 4. num_hidden_layers=6
 5. type_vocab_size=1

## How to Use
You can use this model directly with a pipeline for masked language modeling:

```py
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline

model = AutoModelWithLMHead.from_pretrained("keshan/SinhalaBERTo")
tokenizer = AutoTokenizer.from_pretrained("keshan/SinhalaBERTo")

fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

fill_mask("මම ගෙදර <mask>.")

```


---
tags:
- conversational
---

# Jake Peralta DialoGPT Model


Ver-Online Malignant PELICULA completa En Espanol Latino HD


# XLM-Align

**Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment** (ACL-2021, [paper](https://arxiv.org/pdf/2106.06381.pdf), [github](https://github.com/CZWin32768/XLM-Align))

XLM-Align is a pretrained cross-lingual language model that supports 94 languages. See details in our [paper](https://arxiv.org/pdf/2106.06381.pdf).

## Example

```
model = = AutoModel.from_pretrained("CZWin32768/xlm-align")
```

## Evaluation Results

XTREME cross-lingual understanding tasks:

| Model | POS | NER  | XQuAD | MLQA | TyDiQA | XNLI | PAWS-X | Avg |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
| XLM-R_base | 75.6 | 61.8 | 71.9 / 56.4 | 65.1 / 47.2 | 55.4 / 38.3 | 75.0 | 84.9 | 66.4 |
| XLM-Align | **76.0** | **63.7** | **74.7 / 59.0** | **68.1 / 49.8**  |  **62.1 / 44.8** | **76.2**  | **86.8**  | **68.9** |

## MD5

```
b9d214025837250ede2f69c9385f812c  config.json
6005db708eb4bab5b85fa3976b9db85b  pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046  sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747  tokenizer.json
```

## About

Contact: chizewen\@outlook.com

BibTeX:

```
@article{xlmalign,
  title={Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment},
  author={Zewen Chi and Li Dong and Bo Zheng and Shaohan Huang and Xian-Ling Mao and Heyan Huang and Furu Wei},
  journal={arXiv preprint arXiv:2106.06381},
  year={2021}
}
```


---
tags:
- conversational
---

#Harry Potter DialoGPT Model


---
language: "zh_CN"
thumbnail: "url to a thumbnail used in social sharing"
tags:
- NLP
- LAW
license: "MIT"
datasets:
- WIP
metrics:
- WIP
---


---
tags:
- conversational
---

# Leslie


---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: 1-es-fp16-t5-finetune-updated-git
  results: []
---

<!-- 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. -->

# 1-es-fp16-t5-finetune-updated-git

This model is a fine-tuned version of [Michau/t5-base-en-generate-headline](https://huggingface.co/Michau/t5-base-en-generate-headline) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9820
- Rouge1: 18.523
- Rouge2: 4.5506
- Rougel: 18.4962
- Rougelsum: 18.4927
- Gen Len: 3.2658

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6021        | 1.0   | 843  | 2.9433          | 18.6454 | 4.1174 | 18.6332 | 18.6352   | 3.3681  |
| 2.5685        | 2.0   | 1686 | 2.9808          | 18.5395 | 4.5506 | 18.5061 | 18.5021   | 3.2401  |
| 2.5437        | 3.0   | 2529 | 2.9814          | 18.5347 | 4.5506 | 18.5002 | 18.5004   | 3.2468  |
| 2.5412        | 4.0   | 3372 | 2.9820          | 18.523  | 4.5506 | 18.4962 | 18.4927   | 3.2658  |

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: generate-topic-t5
  results: []
---

<!-- 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. -->

# generate-topic-t5

This model is a fine-tuned version of [Michau/t5-base-en-generate-headline](https://huggingface.co/Michau/t5-base-en-generate-headline) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0811
- Rouge1: 18.3442
- Rouge2: 4.5328
- Rougel: 18.3542
- Rougelsum: 18.3149
- Gen Len: 2.8661

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.483         | 1.0   | 843  | 3.0830          | 19.0804 | 5.0396 | 19.0782 | 19.0958   | 3.1629  |
| 2.4161        | 2.0   | 1686 | 3.0129          | 18.7921 | 4.5651 | 18.7645 | 18.7975   | 3.0703  |
| 2.5267        | 3.0   | 2529 | 3.0748          | 18.4505 | 4.7123 | 18.4473 | 18.4045   | 2.8705  |
| 2.5348        | 4.0   | 3372 | 3.0810          | 18.2924 | 4.4835 | 18.2907 | 18.2668   | 2.8574  |
| 2.5219        | 5.0   | 4215 | 3.0809          | 18.3525 | 4.5328 | 18.3616 | 18.3301   | 2.8661  |
| 2.521         | 6.0   | 5058 | 3.0811          | 18.3442 | 4.5328 | 18.3542 | 18.3149   | 2.8661  |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fullqn-es-fp16-t5-finetune-updated-git
  results: []
---

<!-- 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. -->

# fullqn-es-fp16-t5-finetune-updated-git

This model is a fine-tuned version of [Michau/t5-base-en-generate-headline](https://huggingface.co/Michau/t5-base-en-generate-headline) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5974
- Rouge1: 13.1518
- Rouge2: 1.9593
- Rougel: 13.137
- Rougelsum: 13.1923
- Gen Len: 2.58

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.306         | 1.0   | 843  | 3.0443          | 18.2607 | 4.7939 | 18.2547 | 18.2547   | 3.5621  |
| 2.5777        | 2.0   | 1686 | 2.9389          | 18.6367 | 5.1439 | 18.6377 | 18.6555   | 3.0839  |
| 2.382         | 3.0   | 2529 | 2.9026          | 18.9884 | 5.0808 | 18.9597 | 18.9955   | 3.0267  |
| 2.3196        | 4.0   | 3372 | 2.8671          | 19.1374 | 5.2409 | 19.0865 | 19.1408   | 2.9985  |
| 2.2255        | 5.0   | 4215 | 2.8239          | 19.3383 | 5.2085 | 19.3085 | 19.3103   | 3.0538  |
| 2.405         | 6.0   | 5058 | 3.1016          | 15.749  | 3.1641 | 15.7784 | 15.7941   | 2.7265  |
| 2.6728        | 7.0   | 5901 | 3.4890          | 13.7486 | 2.4258 | 13.7343 | 13.7481   | 2.6309  |
| 3.0525        | 8.0   | 6744 | 3.5974          | 13.1518 | 1.9593 | 13.137  | 13.1923   | 2.58    |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0


---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: WORDED-LABEL-es-fp16-t5-finetune-updated-git
  results: []
---

<!-- 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. -->

# WORDED-LABEL-es-fp16-t5-finetune-updated-git

This model is a fine-tuned version of [Michau/t5-base-en-generate-headline](https://huggingface.co/Michau/t5-base-en-generate-headline) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9194
- Rouge1: 17.2694
- Rouge2: 4.3283
- Rougel: 17.2127
- Rougelsum: 17.1601
- Gen Len: 2.8133

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.2938        | 1.0   | 843  | 2.8796          | 18.6277 | 5.2886 | 18.6389 | 18.6033   | 3.0194  |
| 2.1958        | 2.0   | 1686 | 2.8364          | 19.6262 | 5.6854 | 19.6308 | 19.5831   | 3.0354  |
| 2.2178        | 3.0   | 2529 | 2.9016          | 17.7039 | 4.722  | 17.7002 | 17.6283   | 2.8812  |
| 2.2781        | 4.0   | 3372 | 2.9109          | 17.3042 | 4.4334 | 17.2802 | 17.2237   | 2.8259  |
| 2.2661        | 5.0   | 4215 | 2.9160          | 17.2128 | 4.3283 | 17.1875 | 17.1273   | 2.821   |
| 2.2467        | 6.0   | 5058 | 2.9194          | 17.1852 | 4.3647 | 17.1427 | 17.0865   | 2.8157  |
| 2.2631        | 7.0   | 5901 | 2.9194          | 17.2694 | 4.3283 | 17.2127 | 17.1601   | 2.8133  |

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: with-label-sep-es-fp16-t5-finetune-updated-git
  results: []
---

<!-- 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. -->

# with-label-sep-es-fp16-t5-finetune-updated-git

This model is a fine-tuned version of [Michau/t5-base-en-generate-headline](https://huggingface.co/Michau/t5-base-en-generate-headline) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9825
- Rouge1: 16.2983
- Rouge2: 4.9586
- Rougel: 16.2336
- Rougelsum: 16.238
- Gen Len: 4.0328

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.6405        | 1.0   | 843  | 3.1872          | 16.2299 | 4.6942 | 16.1258 | 16.0936   | 3.2961  |
| 2.6188        | 2.0   | 1686 | 3.0123          | 16.6609 | 4.6374 | 16.5907 | 16.5256   | 4.1331  |
| 2.4263        | 3.0   | 2529 | 2.9764          | 16.6127 | 5.0803 | 16.5155 | 16.5248   | 4.356   |
| 2.4094        | 4.0   | 3372 | 2.9827          | 16.458  | 4.99   | 16.3878 | 16.3829   | 4.0242  |
| 2.4086        | 5.0   | 4215 | 2.9825          | 16.4399 | 4.99   | 16.361  | 16.3678   | 4.0271  |
| 2.3933        | 6.0   | 5058 | 2.9825          | 16.4399 | 4.99   | 16.361  | 16.3678   | 4.0309  |
| 2.4043        | 7.0   | 5901 | 2.9825          | 16.2983 | 4.9586 | 16.2336 | 16.238    | 4.0328  |

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: full-es-fp16-t5-finetune-updated-git
  results: []
---

<!-- 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. -->

# full-es-fp16-t5-finetune-updated-git

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8620
- Rouge1: 19.4662
- Rouge2: 5.4963
- Rougel: 19.4306
- Rougelsum: 19.4777
- Gen Len: 3.0306

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.1426        | 1.0   | 843  | 2.8620          | 19.4662 | 5.4963 | 19.4306 | 19.4777   | 3.0306  |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0


---
tags:
- generated_from_trainer
model-index:
- name: best-1-es-fp16-t5-finetune-updated-git
  results: []
---

<!-- 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. -->

# best-1-es-fp16-t5-finetune-updated-git

This model is a fine-tuned version of [Michau/t5-base-en-generate-headline](https://huggingface.co/Michau/t5-base-en-generate-headline) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_rouge1: 0.0
- eval_rouge2: 0.0
- eval_rougeL: 0.0
- eval_rougeLsum: 0.0
- eval_gen_len: 0.0
- eval_runtime: 115.1335
- eval_samples_per_second: 17.91
- eval_steps_per_second: 1.12
- epoch: 8.0
- step: 6744

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-finetune-config
  results: []
---

<!-- 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. -->

# distilbert-finetune-config

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2489
- Accuracy: 0.3865

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9595        | 1.0   | 3370  | 1.2489          | 0.3865   |
| 0.9057        | 2.0   | 6740  | 1.1566          | 0.3710   |
| 0.9004        | 3.0   | 10110 | 1.2525          | 0.3598   |
| 0.8878        | 4.0   | 13480 | 1.1973          | 0.3681   |
| 0.879         | 5.0   | 16850 | 1.2631          | 0.3661   |
| 0.8803        | 6.0   | 20220 | 1.2263          | 0.3681   |

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: es-fp16-t5-finetune-updated-git
  results: []
---

<!-- 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. -->

# es-fp16-t5-finetune-updated-git

This model is a fine-tuned version of [Michau/t5-base-en-generate-headline](https://huggingface.co/Michau/t5-base-en-generate-headline) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8793
- Rouge1: 18.5326
- Rouge2: 4.8367
- Rougel: 18.5758
- Rougelsum: 18.5291
- Gen Len: 2.9239

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.4292        | 1.0   | 843  | 2.9303          | 18.8138 | 5.2732 | 18.8417 | 18.7819   | 3.3594  |
| 2.2396        | 2.0   | 1686 | 2.8819          | 18.8379 | 5.1665 | 18.8373 | 18.7863   | 3.322   |
| 2.2625        | 3.0   | 2529 | 2.8614          | 18.2076 | 5.2732 | 18.2631 | 18.1866   | 3.0422  |
| 2.2805        | 4.0   | 3372 | 2.8704          | 18.4481 | 5.0897 | 18.4869 | 18.4198   | 2.9617  |
| 2.2722        | 5.0   | 4215 | 2.8794          | 18.4166 | 4.8367 | 18.4564 | 18.3973   | 2.9326  |
| 2.2583        | 6.0   | 5058 | 2.8795          | 18.4644 | 4.8367 | 18.5202 | 18.4473   | 2.9282  |
| 2.2681        | 7.0   | 5901 | 2.8793          | 18.5326 | 4.8367 | 18.5758 | 18.5291   | 2.9239  |

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distil-bert-finetune
  results: []
---

<!-- 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. -->

# distil-bert-finetune

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7149
- Accuracy: 0.6950

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.704         | 1.0   | 1685  | 0.7149          | 0.6950   |
| 0.5377        | 2.0   | 3370  | 0.7132          | 0.6833   |
| 0.5049        | 3.0   | 5055  | 0.8525          | 0.6712   |
| 0.4569        | 4.0   | 6740  | 0.8527          | 0.6746   |
| 0.399         | 5.0   | 8425  | 1.0650          | 0.6382   |
| 0.3797        | 6.0   | 10110 | 1.0089          | 0.6620   |

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


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---
title: Test Space
emoji: 🔥
colorFrom: indigo
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
---

# Configuration

`title`: _string_
Display title for the Space

`emoji`: _string_
Space emoji (emoji-only character allowed)

`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)

`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)

`sdk`: _string_
Can be either `gradio` or `streamlit`

`sdk_version` : _string_
Only applicable for `streamlit` SDK.
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.

`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.

`pinned`: _boolean_
Whether the Space stays on top of your list.


---
tags:
- conversational
---

# White's Bot


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9281908990011098
    - name: Recall
      type: recall
      value: 0.9355632621098557
    - name: F1
      type: f1
      value: 0.9318624993035824
    - name: Accuracy
      type: accuracy
      value: 0.9837641190207635
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0629
- Precision: 0.9282
- Recall: 0.9356
- F1: 0.9319
- Accuracy: 0.9838

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2406        | 1.0   | 878  | 0.0721          | 0.9072    | 0.9172 | 0.9122 | 0.9801   |
| 0.0529        | 2.0   | 1756 | 0.0637          | 0.9166    | 0.9318 | 0.9241 | 0.9826   |
| 0.0315        | 3.0   | 2634 | 0.0629          | 0.9282    | 0.9356 | 0.9319 | 0.9838   |

### Framework versions

- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
model-index:
- name: QAIDeptModel
  results: []
---

<!-- 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. -->

# QAIDeptModel

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the None dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 105  | 2.6675          |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-tamil-colab
  results: []
---

<!-- 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. -->

# wav2vec2-large-xls-r-300m-tamil-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3


---
language: en
thumbnail: https://www.huggingtweets.com/deeperthrill/1616001334930/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

👁 Deep Thrill 😎 🤖 AI Bot

@deeperthrill bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@deeperthrill's tweets](https://twitter.com/deeperthrill).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 2415 |
| Short tweets | 165 |
| Tweets kept | 655 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t139cp8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deeperthrill's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9rc0g39n) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9rc0g39n/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/deeperthrill')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/cdcgov/1619462636252/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

CDC 🤖 AI Bot

@cdcgov bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@cdcgov's tweets](https://twitter.com/cdcgov).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1036 |
| Short tweets | 1 |
| Tweets kept | 2213 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2salp8gg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cdcgov's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tncuzay) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tncuzay/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/cdcgov')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/__justplaying/1616931832539/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

alice, dash of wonderland 🎀 🤖 AI Bot

@__justplaying bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@__justplaying's tweets](https://twitter.com/__justplaying).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3210 |
| Retweets | 706 |
| Short tweets | 518 |
| Tweets kept | 1986 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1og52vt9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @__justplaying's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ir21lg6) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ir21lg6/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/__justplaying')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/acephallus/1617764407342/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

annie halation! 🌺 🤖 AI Bot

@acephallus bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@acephallus's tweets](https://twitter.com/acephallus).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 223 |
| Short tweets | 613 |
| Tweets kept | 2391 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gd83k7hw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @acephallus's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/32kzz65f) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/32kzz65f/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/acephallus')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: http://res.cloudinary.com/huggingtweets/image/upload/v1599932067/tmarysuma.jpg
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Mary Suma (IIM-Bangalore) 🤖 AI Bot

@tmarysuma bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@tmarysuma's tweets](https://twitter.com/tmarysuma).

Data
Quantity

Tweets downloaded
430

Retweets
41

Short tweets
68

Tweets kept
321

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1yrcwt69/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tmarysuma's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/5awg1uqv) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/5awg1uqv/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/tmarysuma')
generator("My dream is", num_return_sequences=5)

### Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/spam_can/1617789719879/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Cay 🏳🌈🐱🏳⚧ 🤖 AI Bot

@spam_can bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@spam_can's tweets](https://twitter.com/spam_can).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3231 |
| Retweets | 1216 |
| Short tweets | 177 |
| Tweets kept | 1838 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1u0hq0wb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @spam_can's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e7i2emb) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e7i2emb/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/spam_can')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/notjohnfante/1614109599350/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

2,300 Guillotines 💀 🤖 AI Bot

@notjohnfante bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@notjohnfante's tweets](https://twitter.com/notjohnfante).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2857 |
| Retweets | 229 |
| Short tweets | 239 |
| Tweets kept | 2389 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3eh51lx0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @notjohnfante's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2nu4o83i) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2nu4o83i/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/notjohnfante')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/rockberta/1602246894182/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Roberta Rocca 🤖 AI Bot

@rockberta bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@rockberta's tweets](https://twitter.com/rockberta).

Data
Quantity

Tweets downloaded
1182

Retweets
464

Short tweets
46

Tweets kept
672

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/ur7lblcc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rockberta's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3ippw9pw) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3ippw9pw/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/rockberta')
generator("My dream is", num_return_sequences=5)

### Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/lavanguardia/1610648853706/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

La Vanguardia 🤖 AI Bot

@lavanguardia bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@lavanguardia's tweets](https://twitter.com/lavanguardia).

Data
Quantity

Tweets downloaded
3208

Retweets
578

Short tweets
51

Tweets kept
2579

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2oy8ll1q/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lavanguardia's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ks3gujo) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ks3gujo/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/lavanguardia')
generator("My dream is", num_return_sequences=5)

### Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/franxxfurt/1617765541385/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

OnlyFranxx 🤖 AI Bot

@franxxfurt bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@franxxfurt's tweets](https://twitter.com/franxxfurt).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3118 |
| Retweets | 1317 |
| Short tweets | 267 |
| Tweets kept | 1534 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1n7v3881/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @franxxfurt's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ax82159) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ax82159/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/franxxfurt')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/clementdelangue/1622219884919/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

clem 🤗

@clementdelangue

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from clem 🤗.

| Data | clem 🤗 |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 1749 |
| Short tweets | 300 |
| Tweets kept | 1190 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2rnflp7g/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @clementdelangue's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3drhbp6u) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3drhbp6u/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/clementdelangue')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/guestyperson/1614136556129/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Jamie Moffatt 🤖 AI Bot

@guestyperson bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@guestyperson's tweets](https://twitter.com/guestyperson).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3152 |
| Retweets | 1179 |
| Short tweets | 192 |
| Tweets kept | 1781 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pvm3v6e/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guestyperson's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nuca4qh) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nuca4qh/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/guestyperson')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/claireredacted/1627519832927/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

Claire

@claireredacted

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Claire.

| Data | Claire |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 518 |
| Short tweets | 616 |
| Tweets kept | 2099 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39vy8r67/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @claireredacted's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/h52pysz1) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/h52pysz1/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/claireredacted')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/pradyuprasad/1630307615715/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

Pradyumna (27/100 blog posts)

@pradyuprasad

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Pradyumna (27/100 blog posts).

| Data | Pradyumna (27/100 blog posts) |
| --- | --- |
| Tweets downloaded | 3225 |
| Retweets | 293 |
| Short tweets | 449 |
| Tweets kept | 2483 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qrkwd1v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pradyuprasad's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/nprezkxg) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/nprezkxg/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/pradyuprasad')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/thefoxjulia/1614104604500/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🦊julia🦊 🤖 AI Bot

@thefoxjulia bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@thefoxjulia's tweets](https://twitter.com/thefoxjulia).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3158 |
| Retweets | 1737 |
| Short tweets | 544 |
| Tweets kept | 877 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bwc2es8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thefoxjulia's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1sghmlbv) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1sghmlbv/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/thefoxjulia')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/barackobama-karlousm-uofofn/1625032946203/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI CYBORG 🤖

University of OnFoeNem 🇭🇹 & Karlous & Barack Obama

@barackobama-karlousm-uofofn

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from University of OnFoeNem 🇭🇹 & Karlous & Barack Obama.

| Data | University of OnFoeNem 🇭🇹 | Karlous | Barack Obama |
| --- | --- | --- | --- |
| Tweets downloaded | 3153 | 3207 | 3249 |
| Retweets | 457 | 1736 | 352 |
| Short tweets | 576 | 175 | 25 |
| Tweets kept | 2120 | 1296 | 2872 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ghcjele/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @barackobama-karlousm-uofofn's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/24cklty4) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/24cklty4/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/barackobama-karlousm-uofofn')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: http://www.huggingtweets.com/xxinnernettexx/1642725567201/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ

@xxinnernettexx

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ.

| Data | poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ |
| --- | --- |
| Tweets downloaded | 1104 |
| Retweets | 340 |
| Short tweets | 126 |
| Tweets kept | 638 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kvu68mt7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xxinnernettexx's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xyct58g) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xyct58g/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/xxinnernettexx')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/nigelthurlow/1626906893945/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

Nigel Thurlow

@nigelthurlow

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Nigel Thurlow.

| Data | Nigel Thurlow |
| --- | --- |
| Tweets downloaded | 1264 |
| Retweets | 648 |
| Short tweets | 27 |
| Tweets kept | 589 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/n4jwj2tf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nigelthurlow's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/nigelthurlow')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/iljone/1616774453050/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

IllinoisJones 🤖 AI Bot

@iljone bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@iljone's tweets](https://twitter.com/iljone).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 337 |
| Retweets | 6 |
| Short tweets | 99 |
| Tweets kept | 232 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3l85ym1p/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iljone's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3uzsj96o) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3uzsj96o/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/iljone')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/homehousesys/1616643362209/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

sn8rl sn8rl gr8wl gr8wl grrrrrrrr 8ark grrrrrrrr 🤖 AI Bot

@homehousesys bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@homehousesys's tweets](https://twitter.com/homehousesys).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 32 |
| Short tweets | 634 |
| Tweets kept | 2578 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ep0ywdr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @homehousesys's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/14mt5icw) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/14mt5icw/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/homehousesys')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/joshuamterry/1614192343266/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Joshua Terry 🤖 AI Bot

@joshuamterry bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@joshuamterry's tweets](https://twitter.com/joshuamterry).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2641 |
| Retweets | 215 |
| Short tweets | 264 |
| Tweets kept | 2162 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10h60169/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @joshuamterry's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p2x7h27) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p2x7h27/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/joshuamterry')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/steashaz/1603197572121/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Stéphanie Laporte 🤖 AI Bot

@steashaz bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@steashaz's tweets](https://twitter.com/steashaz).

Data
Quantity

Tweets downloaded
3224

Retweets
1202

Short tweets
543

Tweets kept
1479

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/s7mezf77/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @steashaz's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1g8rzv0o) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1g8rzv0o/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/steashaz')
generator("My dream is", num_return_sequences=5)

### Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/lp_lapresse/1615924707355/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

La Presse 🤖 AI Bot

@lp_lapresse bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@lp_lapresse's tweets](https://twitter.com/lp_lapresse).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 11 |
| Tweets kept | 3239 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/peh1i6on/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lp_lapresse's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7qbglbj7) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7qbglbj7/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/lp_lapresse')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://www.huggingtweets.com/tomb_respecter/1614137349511/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

tomb respecter 🤖 AI Bot

@tomb_respecter bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@tomb_respecter's tweets](https://twitter.com/tomb_respecter).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3193 |
| Retweets | 196 |
| Short tweets | 90 |
| Tweets kept | 2907 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3oii0g6f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tomb_respecter's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1tdsteqg) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1tdsteqg/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/tomb_respecter')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)


---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---

M⦁͘͜⦁̸̀͘⦁͘ P⦁̸̀͘⦁⦁͘͜⦁͟͞⦁⦁͘⦁͢͜͜⦁́ 🤖 AI Bot

@markprzepiora bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@markprzepiora's tweets](https://twitter.com/markprzepiora).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1093 |
| Retweets | 55 |
| Short tweets | 100 |
| Tweets kept | 938 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e9iu7ts/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

## Training procedure

The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @markprzepiora's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9mk8jcf5) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9mk8jcf5/artifacts) is logged and versioned.

## How to use

You can use this model directly with a pipeline for text generation:

```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/markprzepiora')
generator("My dream is", num_return_sequences=5)
```

## Limitations and bias

The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

In addition, the data present in the user's tweets further affects the text generated by the model.

## About

*Built by Boris Dayma*

[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

For more details, visit the project repository.

[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)