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README.md

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+---
+language: en
+tags:
+- exbert
+license: mit
+---
+
+# ColD Fusion BERT uncased model
+
+Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets. 
+Full details at [this paper](https://arxiv.org/abs/2212.01378).
+
+## Paper Abstract:
+
+Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a 
+mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, 
+massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources 
+that are only available to well-resourced teams.
+
+In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed 
+computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic 
+loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that 
+ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on 
+all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find 
+ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, 
+ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
+
+
+### How to use
+Best way to use is to finetune on your own task, but you can also extract features directly.
+To get the features of a given text in PyTorch:
+
+```python
+from transformers import RobertaTokenizer, RobertaModel
+tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
+model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+and in TensorFlow:
+
+```python
+from transformers import RobertaTokenizer, TFRobertaModel
+tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
+model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+## Evaluation results
+See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
+When fine-tuned on downstream tasks, this model achieves the following results:
+
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{ColDFusion,
+  author    = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
+  title     = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
+  journal   = {CoRR},
+  volume    = {abs/2212.01378},
+  year      = {2022},
+  url       = {https://arxiv.org/abs/2212.01378},
+  archivePrefix = {arXiv},
+  eprint    = {2212.01378},
+}
+```
+
+<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
+	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
+</a>

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c5cd68686c39d7779e87d0388fd8703e97824e3aee9dea71ca1135b2d8e4b24
+size 438006381

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "bert-base-uncased",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}