
<!-- 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. -->

# base

![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png)

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562

## Model description

- **Model type:** Language model
- **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
- **License:** Apache 2.0
- **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5)
- **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints)
- **Resources for more information:**
  - [Research paper](https://arxiv.org/pdf/2210.11416.pdf)
  - [GitHub Repo](https://github.com/google-research/t5x)
  - [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5)

## Intended uses & limitations

The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf):

> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application,

## Training and evaluation data

- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562

## Training procedure
Training procedure example notebook for flan-T5 and pushing it to hub
[https://github.com/EveripediaNetwork/ai/blob/main/notebooks/Fine-Tuning-Flan-T5_1.ipynb](https://github.com/EveripediaNetwork/ai/blob/main/notebooks/Fine-Tuning-Flan-T5_1.ipynb)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: Constant
- num_epochs: 3.0

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
---
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "base", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail 3.0.0", "type": "cnn_dailymail", "config": "3.0.0", "split": "validation", "args": "3.0.0"}, "metrics": [{"type": "rouge", "value": 42.1388, "name": "Rouge1"}]}]}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:cnn_dailymail arxiv:2210.11416 license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #arxiv-2210.11416 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# base

!model image

This model is a fine-tuned version of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562

## Model description

- Model type: Language model
- Language(s) (NLP): English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
- License: Apache 2.0
- Related Models: All FLAN-T5 Checkpoints
- Original Checkpoints: All Original FLAN-T5 Checkpoints
- Resources for more information:
  - Research paper
  - GitHub Repo
  - Hugging Face FLAN-T5 Docs (Similar to T5) 

## Intended uses & limitations

The information below in this section are copied from the model's official model card:

> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application,

## Training and evaluation data

- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562

## Training procedure
Training procedure example notebook for flan-T5 and pushing it to hub
URL

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: Constant
- num_epochs: 3.0

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
---
# base

!model image

This model is a fine-tuned version of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562 ## Model description

- Model type: Language model
- Language(s) (NLP): English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
- License: Apache 2.0
- Related Models: All FLAN-T5 Checkpoints
- Original Checkpoints: All Original FLAN-T5 Checkpoints
- Resources for more information:
  - Research paper
  - GitHub Repo
  - Hugging Face FLAN-T5 Docs (Similar to T5) ## Intended uses & limitations

The information below in this section are copied from the model's official model card:

> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, ## Training and evaluation data

- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562 ## Training procedure
Training procedure example notebook for flan-T5 and pushing it to hub
URL ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: Constant
- num_epochs: 3.0 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
---
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #arxiv-2210.11416 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # base

!model image

This model is a fine-tuned version of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562 ## Model description

- Model type: Language model
- Language(s) (NLP): English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
- License: Apache 2.0
- Related Models: All FLAN-T5 Checkpoints
- Original Checkpoints: All Original FLAN-T5 Checkpoints
- Resources for more information:
  - Research paper
  - GitHub Repo
  - Hugging Face FLAN-T5 Docs (Similar to T5) ## Intended uses & limitations

The information below in this section are copied from the model's official model card:

> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, ## Training and evaluation data

- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562 ## Training procedure
Training procedure example notebook for flan-T5 and pushing it to hub
URL ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: Constant
- num_epochs: 3.0 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
---
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #arxiv-2210.11416 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# base

!model image

This model is a fine-tuned version of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562## Model description

- Model type: Language model
- Language(s) (NLP): English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
- License: Apache 2.0
- Related Models: All FLAN-T5 Checkpoints
- Original Checkpoints: All Original FLAN-T5 Checkpoints
- Resources for more information:
  - Research paper
  - GitHub Repo
  - Hugging Face FLAN-T5 Docs (Similar to T5)## Intended uses & limitations

The information below in this section are copied from the model's official model card:

> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application,## Training and evaluation data

- Loss: 1.4232
- Rouge1: 42.1388
- Rouge2: 19.7696
- Rougel: 30.1512
- Rougelsum: 39.3222
- Gen Len: 71.8562## Training procedure
Training procedure example notebook for flan-T5 and pushing it to hub
URL### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: Constant
- num_epochs: 3.0### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
---