Create README.md

README.md

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+---
+datasets:
+- EleutherAI/pile
+language:
+- en
+pipeline_tag: fill-mask
+tags:
+- summarization
+- translation
+---
+
+# Model Card for T5v2 Base
+
+#  Table of Contents
+
+1. [Model Details](#model-details)
+2. [Uses](#uses)
+3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
+4. [Training Details](#training-details)
+5. [Evaluation](#evaluation)
+6. [Environmental Impact](#environmental-impact)
+7. [Citation](#citation)
+8. [Model Card Authors](#model-card-authors)
+9. [How To Get Started With the Model](#how-to-get-started-with-the-model)
+
+# Model Details
+
+## Model Description
+
+More information needed.
+# Uses
+
+## Direct Use and Downstream Use
+
+More information needed.
+
+## Out-of-Scope Use
+
+More information needed.
+
+# Bias, Risks, and Limitations
+
+More information needed.
+
+## Recommendations
+
+More information needed.
+
+# Training Details
+
+## Training Data
+
+The model was pre-trained on the Pile using an unsupervised denoising objective,
+## Training Procedure
+
+More information needed.
+
+# Evaluation
+
+## Testing Data, Factors & Metrics
+
+More information needed.
+## Results 
+
+More information needed.
+
+# Environmental Impact
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** Google Cloud TPU Pods
+- **Hours used:** More information needed
+- **Cloud Provider:** GCP
+- **Compute Region:** More information needed
+- **Carbon Emitted:** More information needed
+
+# Citation
+
+**BibTeX:**
+
+```bibtex
+@article{2024t5v2,
+  author  = {Lintang Sutawika and Aran Komatsuzaki and Colin Raffel},
+  title   = {T5v2, an update of T5},
+  year    = {2024},
+  url     = {}
+}
+```
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import UMT5Tokenizer, UMT5Model
+
+tokenizer = UMT5Tokenizer.from_pretrained("EleutherAI/t5-v2-base")
+model = UMT5Model.from_pretrained("EleutherAI/t5-v2-base")
+
+input_ids = tokenizer(
+    "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+).input_ids  # Batch size 1
+decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
+
+# forward pass
+outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
+last_hidden_states = outputs.last_hidden_state
+```
+
+
+</details>