Upload README.md

README.md

@@ -1,8 +1,115 @@
 ---
+language:
+- en
+datasets:
+- c4
 tags:
-- t5-new-success
+- deep-narrow
+inference: false
+
+license: apache-2.0
 ---
 
-# Test
-Hf T5: -78.26253414154053
-MTF T5: -78.2625732421875
+# T5-Efficient-BASE-FF1000 (Deep-Narrow version)
+
+T5-Efficient-BASE-FF1000 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).
+It is a *pretrained-only* checkpoint and was released with the
+paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
+by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
+
+In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
+of similar parameter count.
+
+To quote the paper:
+
+> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
+> before considering any other forms of uniform scaling across other dimensions. This is largely due to
+> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
+> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
+> a tall base model might also generally more efficient compared to a large model. We generally find
+> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
+> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
+> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
+> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
+> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
+> consider.
+
+To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
+A sequence of word embeddings is therefore processed sequentially by each transformer block.
+
+## Details model architecture
+
+This model checkpoint - **t5-efficient-base-ff1000** - is of model type **Base** with the following variations:
+- **ff** is **1000**
+
+It has **147.43** million parameters and thus requires *ca.* **589.74 MB** of memory in full precision (*fp32*)
+ or  **294.87 MB** of memory in half precision (*fp16* or *bf16*).
+
+A summary of the *original* T5 model architectures can be seen here:
+
+| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
+| ----| ---- | ---- | ---- | ---- | ---- | ----|
+| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
+| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
+| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
+| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
+| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
+| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
+| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
+
+whereas the following abbreviations are used:
+
+| Abbreviation | Definition |
+| ----| ---- |
+| nl | Number of transformer blocks (depth) |
+| dm | Dimension of embedding vector (output vector of transformers block) |
+| kv | Dimension of key/value projection matrix |
+| nh | Number of attention heads |
+| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | 
+| el | Number of transformer blocks in the encoder (encoder depth) | 
+| dl | Number of transformer blocks in the decoder (decoder depth) | 
+| sh | Signifies that attention heads are shared | 
+| skv | Signifies that key-values projection matrices are tied | 
+
+If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
+
+## Pre-Training
+
+The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using 
+the span-based masked language modeling (MLM) objective.
+
+## Fine-Tuning
+
+**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
+The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
+You can follow on of the following examples on how to fine-tune the model:
+
+*PyTorch*:
+
+- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
+- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
+- [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.
+
+*Tensorflow*:
+
+- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
+- [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.
+
+*JAX/Flax*:
+
+- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
+- [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.
+
+## Downstream Performance
+
+TODO: Add table if available
+
+## Computational Complexity
+
+TODO: Add table if available
+
+## More information
+
+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.
+As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* 
+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.