Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+datasets:
+- statmt/cc100
+base_model:
+- FacebookAI/xlm-roberta-base
 ---
 
-# Model Card for Model ID
+# nomic-xlm-2048: XLM-Roberta Base with RoPE
 
-<!-- Provide a quick summary of what the model is/does. -->
+`nomic-xlm-2048` is a finetuned XLM-Roberta Base model with learned positional embeddings swapped for RoPE and trained for 10k steps on [CC100](https://huggingface.co/datasets/statmt/cc100).
 
 
+`nomic-xlm-2048` performs competitively to other multilingual encoders on GLUE and XTREME-R
 
-## Model Details
+| Model | Params | Pos. | Seq. | Avg. | CoLA | SST-2 | MRPC | STS-B | QQP | MNLI | QNLI | RTE |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| XLM-R-Base | 279M | Abs. | 512 | 82.35 | 46.95 | 92.54 | 87.37 | 89.32 | 90.69 | 84.34 | 90.35 | 77.26 |
+| nomic-xlm-2048 | 278M | RoPE | 2048 | 81.63 | 44.69 | 91.97 | 87.50 | 88.48 | 90.38 | 83.59 | 89.38 | 76.54 |
+| mGTE-Base | 306M | RoPE | 8192 | 80.77 | 27.22 | 91.97 | 89.71 | 89.55 | 91.20 | 85.16 | 90.91 | 80.41 |
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+| Model | Avg. | XNLI | XCOPA | UDPOS | WikiANN | XQuAD | MLQA | TyDiQA-GoldP | Mewsli-X | LAReQA | Tatoeba |
+|---|---|---|---|---|---|---|---|---|---|---|---|
+| XLM-R-Base | 62.31 | 74.49 | 51.8 | 74.33 | 60.99 | 72.96 | 61.45 | 54.31 | 42.45 | 63.49 | 66.79 |
+| nomic-xlm-2048 | 62.70 | 73.57 | 61.71 | 74.92 | 60.96 | 71.13 | 59.61 | 43.46 | 45.27 | 67.49 | 70.82 |
+| mGTE-Base | 64.63 | 73.58 | 63.62 | 73.52 | 60.72 | 74.71 | 63.88 | 49.68 | 44.58 | 71.90 | 70.07 |
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+# Usage
 
-### Model Sources [optional]
+```python
+from transformers import AutoModelForMaskedLM, AutoConfig, AutoTokenizer, pipeline
 
-<!-- Provide the basic links for the model. -->
+tokenizer = AutoTokenizer.from_pretrained('nomic-ai/nomic-xlm-2048') # `nomic-bert-2048` uses the standard BERT tokenizer
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+config = AutoConfig.from_pretrained('nomic-ai/nomic-xlm-2048', trust_remote_code=True) # the config needs to be passed in
+model = AutoModelForMaskedLM.from_pretrained('nomic-ai/nomic-xlm-2048',config=config, trust_remote_code=True)
 
-## Uses
+# To use this model directly for masked language modeling
+classifier = pipeline('fill-mask', model=model, tokenizer=tokenizer,device="cpu")
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+print(classifier("I [MASK] to the store yesterday."))
+```
+To finetune the model for a Sequence Classification task, you can use the following snippet
 
-### Direct Use
+```python
+from transformers import AutoConfig, AutoModelForSequenceClassification
+model_path = "nomic-ai/nomic-xlm-2048"
+config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+# strict needs to be false here since we're initializing some new params
+model = AutoModelForSequenceClassification.from_pretrained(model_path, config=config, trust_remote_code=True, strict=False)
+```
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+# Join the Nomic Community
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- Nomic: [https://nomic.ai](https://nomic.ai)
+- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
+- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)