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  ---
 
 
 
 
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  tags:
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- - t5-new-success
 
 
 
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  ---
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- # Test
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- Hf T5: -69.08134460449219
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- MTF T5: -69.08140563964844
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+ datasets:
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+ - c4
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  tags:
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+ - deep-narrow
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+ inference: false
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+
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+ license: apache-2.0
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  ---
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+ # T5-Efficient-LARGE-DL16 (Deep-Narrow version)
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+
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+ T5-Efficient-LARGE-DL16 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
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+ It is a *pretrained-only* checkpoint and was released with the
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+ paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
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+ by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
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+
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+ In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
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+ of similar parameter count.
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+
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+ To quote the paper:
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+
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+ > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
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+ > before considering any other forms of uniform scaling across other dimensions. This is largely due to
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+ > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
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+ > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
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+ > a tall base model might also generally more efficient compared to a large model. We generally find
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+ > that, regardless of size, even if absolute performance might increase as we continue to stack layers,
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+ > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
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+ > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
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+ > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
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+ > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
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+ > consider.
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+
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+ To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
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+ A sequence of word embeddings is therefore processed sequentially by each transformer block.
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+
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+ ## Details model architecture
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+
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+ This model checkpoint - **t5-efficient-large-dl16** - is of model type **Large** with the following variations:
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+ - **dl** is **16**
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+
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+ It has **603.47** million parameters and thus requires *ca.* **2413.88 MB** of memory in full precision (*fp32*)
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+ or **1206.94 MB** of memory in half precision (*fp16* or *bf16*).
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+
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+ A summary of the *original* T5 model architectures can be seen here:
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+
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+ | Model | nl (el/dl) | ff | dm | kv | nh | #Params|
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+ | ----| ---- | ---- | ---- | ---- | ---- | ----|
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+ | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
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+ | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
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+ | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
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+ | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
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+ | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
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+ | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
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+ | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
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+
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+ whereas the following abbreviations are used:
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+
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+ | Abbreviation | Definition |
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+ | ----| ---- |
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+ | nl | Number of transformer blocks (depth) |
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+ | dm | Dimension of embedding vector (output vector of transformers block) |
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+ | kv | Dimension of key/value projection matrix |
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+ | nh | Number of attention heads |
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+ | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
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+ | el | Number of transformer blocks in the encoder (encoder depth) |
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+ | dl | Number of transformer blocks in the decoder (decoder depth) |
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+ | sh | Signifies that attention heads are shared |
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+ | skv | Signifies that key-values projection matrices are tied |
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+
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+ If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
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+
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+ ## Pre-Training
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+
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+ The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
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+ the span-based masked language modeling (MLM) objective.
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+
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+ ## Fine-Tuning
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+
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+ **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
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+ The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
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+ You can follow on of the following examples on how to fine-tune the model:
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+
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+ *PyTorch*:
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+
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+ - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
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+ - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
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+ - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
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+
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+ *Tensorflow*:
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+
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+ - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
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+ - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
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+
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+ *JAX/Flax*:
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+
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+ - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
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+ - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
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+
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+ ## Downstream Performance
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+
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+ TODO: Add table if available
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+
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+ ## Computational Complexity
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+
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+ TODO: Add table if available
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+
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+ ## More information
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+
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+ We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
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+ As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
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+ model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.