kiddothe2b
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Browse files- README.md +113 -0
- all_results.json +13 -0
- config.json +44 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
README.md
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---
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license: cc-by-nc-sa-4.0
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---
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---
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license: cc-by-nc-sa-4.0
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pipeline_tag: fill-mask
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language: en
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tags:
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- long_documents
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datasets:
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- c4
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model-index:
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- name: kiddothe2b/longformer-mini-1024
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results: []
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---
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# Longformer / longformer-mini-1024
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## Model description
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[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents. This version of Longformer is presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/xxx).
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The model has been warm-started re-using the weights of miniature BERT [(Turc et al., 2019)](https://arxiv.org/abs/1908.08962), and continued pre-trained for MLM following the paradigm of Longformer released by [Beltagy et al. (2020)](](https://arxiv.org/abs/1908.08962)). It supports sequences of length up to 1,024.
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Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.
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## Intended uses & limitations
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You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
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See the [model hub](https://huggingface.co/models?filter=longformer) to look for fine-tuned versions on a task that
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interests you.
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Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification or question answering.
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## How to use
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You can use this model directly with a pipeline for masked language modeling:
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```python
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from transformers import pipeline
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mlm_model = pipeline('fill-mask', model='kiddothe2b/longformer-mini-1024', trust_remote_code=True)
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mlm_model("Hello I'm a <mask> model.")
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```
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You can also fine-tun it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
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```python
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from transformers import AutoTokenizer, AutoModelforSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/longformer-mini-1024", trust_remote_code=True)
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doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/longformer-mini-1024', trust_remote_code=True)
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```
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## Limitations and bias
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The training data used for this model contains a lot of unfiltered content from the internet, which is far from
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neutral. Therefore, the model can have biased predictions.
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## Training procedure
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### Training and evaluation data
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The model has been warm-started from [google/bert_uncased_L-6_H-256_A-4](https://huggingface.co/google/bert_uncased_L-6_H-256_A-4) checkpoint and has been continued pre-trained for additional 50k steps on English [Wikipedia](https://huggingface.co/datasets/wikipedia).
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- training_steps: 50000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 1.7067 | 0.2 | 10000 | 1.5923 | 0.6714 |
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| 1.6532 | 0.4 | 20000 | 1.5494 | 0.6784 |
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| 1.622 | 0.6 | 30000 | 1.5208 | 0.6830 |
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| 1.588 | 0.8 | 40000 | 1.4880 | 0.6876 |
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| 1.5682 | 1.0 | 50000 | 1.4680 | 0.6908 |
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### Framework versions
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- Transformers 4.19.0.dev0
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- Pytorch 1.11.0
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- Datasets 2.0.0
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- Tokenizers 0.11.6
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## Citing
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If you use this Longformer model in your research, please cite [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/xxx), alongside [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150).
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```
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@misc{chalkidis-etal-2022-hat,
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url = {https://arxiv.org/abs/xxx},
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author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
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title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
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publisher = {arXiv},
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year = {2022},
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}
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@article{Beltagy2020Longformer,
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title={Longformer: The Long-Document Transformer},
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author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
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journal={arXiv:2004.05150},
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year={2020},
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}
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```
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all_results.json
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{
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"epoch": 1.14,
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"eval_accuracy": 0.5881730101414021,
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"eval_loss": 2.206639528274536,
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"eval_runtime": 4720.0655,
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"eval_samples_per_second": 105.931,
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"eval_steps_per_second": 3.31,
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"perplexity": 9.08513469908241,
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"train_loss": 2.444826105957031,
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"train_runtime": 111349.358,
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"train_samples_per_second": 57.477,
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"train_steps_per_second": 0.449
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}
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config.json
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{
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"_name_or_path": "kiddothe2b/longformer-mini-1024",
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"architectures": [
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"LongformerForMaskedLM"
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],
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"attention_mode": "longformer",
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"attention_probs_dropout_prob": 0.1,
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"attention_window": [
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128,
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128,
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128,
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128,
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128,
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128
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],
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"bos_token_id": null,
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"classifier_dropout": null,
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"cls_token_id": 101,
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"eos_token_id": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 256,
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"ignore_attention_mask": false,
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"initializer_range": 0.02,
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"intermediate_size": 1024,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 1026,
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"max_sentence_length": 128,
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"max_sentence_size": 128,
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"max_sentences": 8,
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"model_max_length": 1024,
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"model_type": "longformer",
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"num_attention_heads": 4,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"sep_token_id": 102,
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"torch_dtype": "float32",
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"transformers_version": "4.18.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:cedb7eb8d017b78e84502a37013d0a7d7864f0c95a7b3bbe74758fbbf627522a
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size 56444323
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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tokenizer_config.json
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "data/PLMs/longformer", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
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vocab.txt
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