

# checkpoints

- This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the `vblagoje/lfqa` dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with [t5-base](https://huggingface.co/pszemraj/t5-base-askscience) on the standard eli5 dataset.
  - This checkpoint does seem to be more coherent than t5-base on the original dataset.
- Compared to [bart on lfqa](https://huggingface.co/vblagoje/bart_lfqa), it seems to be able to respond to some questions independently of retrieval.

> NOTE: the inference API is limited to generating approx. 64 chars for runtime reasons, for longer outputs try using it in python as a transformers pipeline object.

## Intended uses & limitations

- Q&A, information retrieval
- it is probably better to use it with a [retrieval pipeline](https://github.com/deepset-ai/haystack) than alone

## Training and evaluation data

- see linked dataset. the dataset was filtered to only included the `askscience` subreddit in an attempt to focus on academic/technical queries.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0

{"language": ["en"], "license": "apache-2.0", "tags": ["t5", "qa", "askscience", "lfqa", "information retrieval"], "datasets": ["vblagoje/lfqa"], "metrics": ["rouge"], "widget": [{"text": "why hasn't humanity expanded to live on other planets in our solar system?", "example_title": "solar system"}, {"text": "question: what is a probability distribution? context: I am just learning about statistics.", "example_title": "probability distribution"}, {"text": "question: What are the underlying physical processes by which exercise helps us lose weight? context: I started working out two weeks ago and already feel a lot better, and started to think about it and became deeply confused.", "example_title": "pumpen"}, {"text": "what is a neural network?", "example_title": "deep learning"}, {"text": "What is the process that computers use to understand human language in deep learning models?", "example_title": "NLP"}], "inference": {"parameters": {"max_length": 64, "no_repeat_ngram_size": 2, "encoder_no_repeat_ngram_size": 4, "repetition_penalty": 3.51, "length_penalty": 0.8, "num_beams": 4, "early_stopping": true}}, "base_model": "google/t5-v1_1-base"}
transformers pytorch safetensors t5 text2text-generation qa askscience lfqa information retrieval en dataset:vblagoje/lfqa base_model:google/t5-v1_1-base license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-vblagoje/lfqa #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 



# checkpoints

- This model is a fine-tuned version of google/t5-v1_1-base on the 'vblagoje/lfqa' dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with t5-base on the standard eli5 dataset.
  - This checkpoint does seem to be more coherent than t5-base on the original dataset.
- Compared to bart on lfqa, it seems to be able to respond to some questions independently of retrieval.

> NOTE: the inference API is limited to generating approx. 64 chars for runtime reasons, for longer outputs try using it in python as a transformers pipeline object.

## Intended uses & limitations

- Q&A, information retrieval
- it is probably better to use it with a retrieval pipeline than alone

## Training and evaluation data

- see linked dataset. the dataset was filtered to only included the 'askscience' subreddit in an attempt to focus on academic/technical queries.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0

# checkpoints

- This model is a fine-tuned version of google/t5-v1_1-base on the 'vblagoje/lfqa' dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with t5-base on the standard eli5 dataset.
  - This checkpoint does seem to be more coherent than t5-base on the original dataset.
- Compared to bart on lfqa, it seems to be able to respond to some questions independently of retrieval.

> NOTE: the inference API is limited to generating approx. 64 chars for runtime reasons, for longer outputs try using it in python as a transformers pipeline object. ## Intended uses & limitations

- Q&A, information retrieval
- it is probably better to use it with a retrieval pipeline than alone ## Training and evaluation data

- see linked dataset. the dataset was filtered to only included the 'askscience' subreddit in an attempt to focus on academic/technical queries. ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2 ### Training results ### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-vblagoje/lfqa #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # checkpoints

- This model is a fine-tuned version of google/t5-v1_1-base on the 'vblagoje/lfqa' dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with t5-base on the standard eli5 dataset.
  - This checkpoint does seem to be more coherent than t5-base on the original dataset.
- Compared to bart on lfqa, it seems to be able to respond to some questions independently of retrieval.

> NOTE: the inference API is limited to generating approx. 64 chars for runtime reasons, for longer outputs try using it in python as a transformers pipeline object. ## Intended uses & limitations

- Q&A, information retrieval
- it is probably better to use it with a retrieval pipeline than alone ## Training and evaluation data

- see linked dataset. the dataset was filtered to only included the 'askscience' subreddit in an attempt to focus on academic/technical queries. ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2 ### Training results ### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-vblagoje/lfqa #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# checkpoints

- This model is a fine-tuned version of google/t5-v1_1-base on the 'vblagoje/lfqa' dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with t5-base on the standard eli5 dataset.
  - This checkpoint does seem to be more coherent than t5-base on the original dataset.
- Compared to bart on lfqa, it seems to be able to respond to some questions independently of retrieval.

> NOTE: the inference API is limited to generating approx. 64 chars for runtime reasons, for longer outputs try using it in python as a transformers pipeline object.## Intended uses & limitations

- Q&A, information retrieval
- it is probably better to use it with a retrieval pipeline than alone## Training and evaluation data

- see linked dataset. the dataset was filtered to only included the 'askscience' subreddit in an attempt to focus on academic/technical queries.## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2### Training results