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# Flan_t5_Large_Chat_Summary

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the shared_TaskA dataset.


## 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: 5

### Example Uses 

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM 
tokenizer_pre = AutoTokenizer.from_pretrained("Amalq/flan_t5_large_chat_summary")
model_pre = AutoModelForSeq2SeqLM.from_pretrained("Amalq/flan_t5_large_chat_summary")
```
{"language": ["en"], "license": "apache-2.0", "datasets": ["Amalq/shared_TaskA"]}
transformers pytorch t5 text2text-generation en dataset:Amalq/shared_TaskA license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-Amalq/shared_TaskA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 






# Flan_t5_Large_Chat_Summary

This model is a fine-tuned version of google/flan-t5-large on the shared_TaskA dataset.


## 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: 5

### Example Uses 


# Flan_t5_Large_Chat_Summary

This model is a fine-tuned version of google/flan-t5-large on the shared_TaskA dataset. ## 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: 5 ### Example Uses
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-Amalq/shared_TaskA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Flan_t5_Large_Chat_Summary

This model is a fine-tuned version of google/flan-t5-large on the shared_TaskA dataset. ## 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: 5 ### Example Uses
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-Amalq/shared_TaskA #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Flan_t5_Large_Chat_Summary

This model is a fine-tuned version of google/flan-t5-large on the shared_TaskA dataset.## 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: 5### Example Uses