license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2217 - Accuracy: 0.924 - F1: 0.9241 | 47158314224dc5724e1c6d7b1b915c58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8136 | 1.0 | 250 | 0.3140 | 0.902 | 0.8998 | | 0.2501 | 2.0 | 500 | 0.2217 | 0.924 | 0.9241 | | 53aa2d0dadbc54c52e85a66a9bfc7220 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | model_zu-en_updated This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus-mt-mul-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8306 - Bleu: 27.1218 | 78dd2a34b9744a3e1d77185b2862cbb7 |
mit | ['pytorch', 'deberta', 'deberta-v2', 'question-answering', 'question answering', 'squad'] | false | How to use 使い方 transformersおよびpytorch、sentencepiece、Juman++をインストールしてください。 以下のコードを実行することで、Question-Answeringタスクを解かせることができます。 please execute this code. ```python import torch from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-base-japanese') model=AutoModelForQuestionAnswering.from_pretrained('Mizuiro-sakura/deberta-v2-base-japanese-finetuned-QAe') | 42f1030bec2fc8d7be08ac527804ead8 |
creativeml-openrail-m | ['text-to-image', 'v2.0', 'Embedding'] | false | Textual Inversion embedding trained on 768x768 images from 80s box arts of Transformers and GIJoe toys and identical sources. *Install by downloading the embedding, and put it in the **\embeddings** folder.*   <table> <tr> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670510933114-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670510933101-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670510933119-6364e6c712188d67e653853e.png"></th> </tr> </table> <table> <tr> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670512344753-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670510933127-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670510933072-6364e6c712188d67e653853e.png"></th> </tr> </table> <table> <tr> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670512585719-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670512585692-6364e6c712188d67e653853e.png"></th> </tr> </table> <table> <tr> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670512585843-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670512585826-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670513448009-6364e6c712188d67e653853e.png"></th> </tr> </table> <table> <tr> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670513239492-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670513239526-6364e6c712188d67e653853e.png"></th> </tr> </table> <table> <tr> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670513384195-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670513330528-6364e6c712188d67e653853e.png"></th> <th><img src="https://s3.amazonaws.com/moonup/production/uploads/1670513330573-6364e6c712188d67e653853e.png"></th> </tr> </table> All images rendered in SD v2.1 | e1a7374cab27b20c2c76cc075b7c026a |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-DM256 (Deep-Narrow version) T5-Efficient-BASE-DM256 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. | cb6754c086ad84fc31d7af9b70986966 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-dm256** - is of model type **Base** with the following variations: - **dm** is **256** It has **74.33** million parameters and thus requires *ca.* **297.32 MB** of memory in full precision (*fp32*) or **148.66 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 | | ab308973305254161196e427cc45f1f0 |
mit | [] | false | Base model: [gpt2-large](https://huggingface.co/gpt2-large) Fine-tuned to generate responses on a dataset of [Vaccine public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). This checkpoint corresponds to the lowest validation perplexity (2.82 at 2 epochs) seen during training. See Training metrics for Tensorboard logs. For input format and usage examples, see our [COVID-19 public health tweet response model](https://huggingface.co/TheRensselaerIDEA/gpt2-large-covid-tweet-response). | afa5428faa9895e15dcb2838b4a0a6ae |
apache-2.0 | ['generated_from_trainer'] | false | model_output_en_de This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1298 - Bleu: 33.9121 - Gen Len: 76.8132 | 0af65a56a06fb68b1d317c769c1c162f |
apache-2.0 | ['generated_from_trainer'] | false | BERT-tiny-sst2 This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4422 - Accuracy: 0.8372 | 65b75d970296b996d4f676dd3fad848b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3914 | 1.0 | 4210 | 0.4383 | 0.8211 | | 0.2577 | 2.0 | 8420 | 0.4422 | 0.8372 | | 0.212 | 3.0 | 12630 | 0.5460 | 0.8085 | | 0.1862 | 4.0 | 16840 | 0.5885 | 0.8245 | | 0.1671 | 5.0 | 21050 | 0.7159 | 0.8096 | | a19c2d1102694a61bc029118857b701d |
mit | ['generated_from_trainer'] | false | Klassifizierung-Gewerke This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0398 - F1: 0.9931 | c5da901eb8daf6f26fddd17952e0ba59 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1473 | 1.0 | 726 | 0.0952 | 0.9822 | | 0.0252 | 2.0 | 1452 | 0.0488 | 0.9918 | | 0.028 | 3.0 | 2178 | 0.0398 | 0.9931 | | 274821a0328101b96168f85afdc874da |
apache-2.0 | ['translation'] | false | opus-mt-sv-fj * source languages: sv * target languages: fj * OPUS readme: [sv-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-fj/opus-2020-01-21.zip) * test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-fj/opus-2020-01-21.test.txt) * test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-fj/opus-2020-01-21.eval.txt) | 4e680be3f78f35206897e7ae3c43a1c8 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-es_s859 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | fbd63ebdf4a12c7f76e0c29e25429d25 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-mse-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1108 - Rouge1: 43.1145 - Rouge2: 23.2262 - Rougel: 37.218 - Rougelsum: 41.0897 - Bleurt: -0.8051 - Gen Len: 18.549 | fbea9429d19330bad5d1fe0f6c842cef |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | aa2375286368ba00dcec6d02ac12f34e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleurt | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:| | 1.5207 | 1.0 | 267 | 1.2922 | 38.8738 | 19.1958 | 32.8458 | 36.9993 | -0.9061 | 18.668 | | 1.363 | 2.0 | 534 | 1.2340 | 39.8466 | 20.0452 | 33.9101 | 37.7708 | -0.8925 | 18.657 | | 1.3062 | 3.0 | 801 | 1.2057 | 40.5536 | 20.8249 | 34.5221 | 38.4648 | -0.8625 | 18.602 | | 1.272 | 4.0 | 1068 | 1.1782 | 41.0078 | 21.2186 | 35.0101 | 38.9186 | -0.8595 | 18.602 | | 1.2312 | 5.0 | 1335 | 1.1688 | 41.521 | 21.7934 | 35.704 | 39.4718 | -0.842 | 18.486 | | 1.2052 | 6.0 | 1602 | 1.1557 | 42.1037 | 22.4291 | 36.3554 | 40.1124 | -0.8432 | 18.533 | | 1.1842 | 7.0 | 1869 | 1.1440 | 42.4438 | 22.6456 | 36.5729 | 40.3134 | -0.8288 | 18.553 | | 1.1643 | 8.0 | 2136 | 1.1408 | 42.245 | 22.4859 | 36.3637 | 40.2193 | -0.8284 | 18.622 | | 1.1495 | 9.0 | 2403 | 1.1320 | 42.5362 | 22.5034 | 36.5092 | 40.4552 | -0.8211 | 18.57 | | 1.1368 | 10.0 | 2670 | 1.1301 | 42.5159 | 22.462 | 36.4646 | 40.3968 | -0.819 | 18.538 | | 1.1203 | 11.0 | 2937 | 1.1243 | 42.2803 | 22.5963 | 36.3454 | 40.2987 | -0.8242 | 18.522 | | 1.1116 | 12.0 | 3204 | 1.1197 | 42.8078 | 22.8409 | 36.7344 | 40.8186 | -0.821 | 18.565 | | 1.099 | 13.0 | 3471 | 1.1193 | 42.7423 | 22.9397 | 36.7894 | 40.7298 | -0.8125 | 18.552 | | 1.0976 | 14.0 | 3738 | 1.1176 | 42.9002 | 23.2394 | 37.0215 | 40.9211 | -0.8156 | 18.568 | | 1.0816 | 15.0 | 4005 | 1.1133 | 43.0007 | 23.3093 | 37.2037 | 40.9719 | -0.8059 | 18.519 | | 1.084 | 16.0 | 4272 | 1.1146 | 42.9053 | 23.2391 | 37.0542 | 40.8826 | -0.8104 | 18.533 | | 1.0755 | 17.0 | 4539 | 1.1124 | 43.0429 | 23.2773 | 37.1389 | 41.0755 | -0.8086 | 18.544 | | 1.0748 | 18.0 | 4806 | 1.1121 | 43.2243 | 23.4179 | 37.2039 | 41.143 | -0.8048 | 18.548 | | 1.072 | 19.0 | 5073 | 1.1106 | 43.1776 | 23.3061 | 37.3105 | 41.1392 | -0.8039 | 18.549 | | 1.0671 | 20.0 | 5340 | 1.1108 | 43.1145 | 23.2262 | 37.218 | 41.0897 | -0.8051 | 18.549 | | e2f108e7c0da53b4462b4f5fae8e792f |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-small-dequad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 97f34663faaec3d03a18acc8f4829a4c |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-dequad-qg") output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>") ``` | 7311d1cdfa8c107ff9cbb6b0c0715bf2 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-dequad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 79.9 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | Bleu_1 | 10.18 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | Bleu_2 | 4.02 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | Bleu_3 | 1.6 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | Bleu_4 | 0.43 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | METEOR | 11.47 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | MoverScore | 54.64 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | ROUGE_L | 10.08 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-small-dequad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_dequad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 90.55 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedF1Score (MoverScore) | 64.33 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedPrecision (BERTScore) | 90.59 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedPrecision (MoverScore) | 64.37 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedRecall (BERTScore) | 90.51 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedRecall (MoverScore) | 64.29 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-small-dequad-ae`](https://huggingface.co/lmqg/mt5-small-dequad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-dequad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_dequad.default.lmqg_mt5-small-dequad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 81.19 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedF1Score (MoverScore) | 54.3 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedPrecision (BERTScore) | 80 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedPrecision (MoverScore) | 54.04 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedRecall (BERTScore) | 82.46 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | QAAlignedRecall (MoverScore) | 54.59 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | | 5025dc1f89a11c7149f98e24c2a9996f |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_dequad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 11 - batch: 16 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-dequad-qg/raw/main/trainer_config.json). | 623b7862507b9a23029a2bfa91b9620e |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-am This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4128 - Precision: 0.0054 - Recall: 0.0166 - F1: 0.0082 - Accuracy: 0.8423 | 2863eb155ef3e0cab9ae1fcc5bf71c3a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 167 | 0.4448 | 0.0 | 0.0 | 0.0 | 0.8573 | | No log | 2.0 | 334 | 0.4078 | 0.0009 | 0.0013 | 0.0011 | 0.8572 | | 0.4231 | 3.0 | 501 | 0.4128 | 0.0054 | 0.0166 | 0.0082 | 0.8423 | | 9269fb7e6c8f4221c455c8b326989697 |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Usage ```python from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("juancopi81/test-audio-diffusion-electronic") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) ``` | 594df111954ee1bdb84e9dd8b7823961 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-subjqa-vanilla-movies-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 1a12b4ac1538836964be7fc59121c5bc |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (movies) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | a30cdb289de77ab3814f750b564d7435 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-large-subjqa-vanilla-movies-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 4d83d031af67cc14518c48a9d4228935 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-subjqa-vanilla-movies-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:-----------------------------------------------------------------| | BERTScore | 93.42 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 24.43 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 16.31 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 7.65 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 4.81 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 20.01 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 61.02 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 25.77 | movies | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | 4256baf9fc42df316ceceb9d32f2fdce |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: movies - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 3 - batch: 8 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-large-subjqa-vanilla-movies-qg/raw/main/trainer_config.json). | a1547047ce80af7d45a871f57a7d9086 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | 8528-diffusion final 8528-diffusion is a latent text-to-image diffusion model, conditioned by fine-tuning to colorful character images. 8528 Diffusion is a fine-tuning model of Stable Diffusion v1.4 with AI output images (t2i and t2i with i2i). I recommend entering "low quality,worst quality," for Negative prompt and Clip skip: 2. <img src=https://i.imgur.com/vCn02tM.jpg > ((ultra-detailed)), ((illustration)), Silver hair, red eyes, beautiful eyes, dress, Queen,Anime style, pretty face, pretty eyes, pretty, girl,High resolution, beautiful girl,octane render, realistic, hyper detailed ray tracing, 8k,classic style,Rococo Negative prompt: (low quality, worst quality:1.4) concept art Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 241379229, Size: 512x768, Model hash: 31cd036c, Clip skip: 2 | 6409fe6f1c7b20bfe97c2031e98644c6 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | 8528-diffusion v0.2 8528-diffusion is a latent text-to-image diffusion model, conditioned by fine-tuning to colorful character images. 8528 Diffusion v0.2 & v0.1 is a fine-tuning model of Waifu Diffusion with AI output images (t2i and t2i with i2i). <img src=https://i.imgur.com/z4sFctp.png > | c12676419e52b353cce78a4b9e28b8c7 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Vietnamese This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 vi dataset. It achieves the following results on the evaluation set: - Loss: 0.7136 - Wer: 15.4925 | 8ca08263b83f4048cfea637b269f5fc5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0001 | 124.0 | 1000 | 0.7136 | 15.4925 | | 0.0001 | 249.0 | 2000 | 0.8532 | 17.0045 | | 0.0 | 374.0 | 3000 | 0.9251 | 19.0972 | | 0.0 | 499.0 | 4000 | 0.9787 | 21.5953 | | 0.0 | 624.0 | 5000 | 0.9921 | 21.4638 | | 0465026972255f8225fa59ea27cf8c10 |
apache-2.0 | [] | false | doc2query/msmarco-t5-small-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini.
- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
| 410b5ce4abd92e9d9566b612ab7e2df5 |
apache-2.0 | [] | false | Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/msmarco-t5-small-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt')
outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
num_return_sequences=5)
print("Text:")
print(text)
print("\nGenerated Queries:")
for i in range(len(outputs)):
query = tokenizer.decode(outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
```
**Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it.
| 108215d1c02af2aa39714a64765c5bbe |
apache-2.0 | [] | false | Training
This model fine-tuned [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (query, passage) from the [MS MARCO Passage-Ranking dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking).
| 30eceefcce2ed485c73f30fc25b9a508 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Low Poly Game Building on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Game Building concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_game_building** | b3a63514180e44b3543558d6e164bb8d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model:  | cc3461c5bae6580c066bd41befb65bca |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/guutv) | a751f82bda4f79651b38ae7c94b9215e |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  | 6c34475aa3e6193938a7da5bb7327dd6 |
apache-2.0 | ['multiberts', 'multiberts-seed_5'] | false | MultiBERTs - Seed 5 MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 2772b4b476e9ca95b76982b610d00fd1 |
apache-2.0 | ['multiberts', 'multiberts-seed_5'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_5') model = TFBertModel.from_pretrained("google/multiberts-seed_5") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_5') model = BertModel.from_pretrained("google/multiberts-seed_5") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 30261360b70f2e773275f9ed2108ebc2 |
apache-2.0 | ['generated_from_trainer', 'nlu', 'text-classification'] | false | bert-base-uncased-amazon-massive-intent This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on [Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset). It achieves the following results on the evaluation set: - Loss: 0.4897 - Accuracy: 0.8903 - F1: 0.8903 | c70740bba27ab944f7c485fd24ca0f48 |
apache-2.0 | ['generated_from_trainer', 'nlu', 'text-classification'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 2.5862 | 1.0 | 720 | 1.0160 | 0.8096 | 0.8096 | | 1.0591 | 2.0 | 1440 | 0.6003 | 0.8716 | 0.8716 | | 0.4151 | 3.0 | 2160 | 0.5113 | 0.8859 | 0.8859 | | 0.3028 | 4.0 | 2880 | 0.5030 | 0.8883 | 0.8883 | | 0.1852 | 5.0 | 3600 | 0.4897 | 0.8903 | 0.8903 | | 67e79bd428d046c66567e8205c142a7f |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Model Description Megatron-GPT 5B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 5B refers to the total trainable parameter count (5 Billion) [1, 2]. This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html). | 23d4ee8d797da32bc8a199de0485a9bb |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Step 1: Install NeMo and dependencies You will need to install NVIDIA Apex and NeMo. ``` git clone https://github.com/ericharper/apex.git cd apex git checkout nm_v1.11.0 pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./ ``` ``` pip install nemo_toolkit['nlp']==1.11.0 ``` Alternatively, you can use NeMo Megatron training docker container with all dependencies pre-installed. | 8b9e8e4e688f74bc6008e51d58e550a4 |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Step 2: Launch eval server **Note.** The example below launches a model variant with Tensor Parallelism (TP) of 2 and Pipeline Parallelism (PP) of 1 on two GPUs. ``` git clone https://github.com/NVIDIA/NeMo.git cd NeMo/examples/nlp/language_modeling git checkout v1.11.0 python megatron_gpt_eval.py gpt_model_file=nemo_gpt5B_fp16_tp2.nemo server=True tensor_model_parallel_size=2 trainer.devices=2 ``` | 81b5899625e438410cb3a624aeca22a3 |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Step 3: Send prompts to your model! ```python import json import requests port_num = 5555 headers = {"Content-Type": "application/json"} def request_data(data): resp = requests.put('http://localhost:{}/generate'.format(port_num), data=json.dumps(data), headers=headers) sentences = resp.json()['sentences'] return sentences data = { "sentences": ["Tell me an interesting fact about space travel."]*1, "tokens_to_generate": 50, "temperature": 1.0, "add_BOS": True, "top_k": 0, "top_p": 0.9, "greedy": False, "all_probs": False, "repetition_penalty": 1.2, "min_tokens_to_generate": 2, } sentences = request_data(data) print(sentences) ``` | 7fd32a4e18a79a36f08c668d8d410693 |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Evaluation results *Zero-shot performance.* Evaluated using [LM Evaluation Test Suite from AI21](https://github.com/AI21Labs/lm-evaluation) | ARC-Challenge | ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA | | ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- | | 0.3976 | 0.5566 | 0.5007 | 0.4171 | 0.6133 | 0.5812 | 0.6356 | 0.6298 | 0.7492 | | 6b46fc6341f89602521278e57712703d |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | References [1] [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) [2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) [4] [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027) | 30a79298da106c4a84c69a297f463344 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2265 - Accuracy: 0.9235 - F1: 0.9237 | e8589d2c3091b7ea78f1153bc7804025 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8243 | 1.0 | 250 | 0.3199 | 0.906 | 0.9025 | | 0.2484 | 2.0 | 500 | 0.2265 | 0.9235 | 0.9237 | | 930f18f192922b557751dedbd345765d |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-ROBERTaBECAS This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.5760 | ca2d481f3aac728cecaf94a6261cf845 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 11 - eval_batch_size: 11 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 9b3f02df78059e14c0874fd705a312f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 4.3366 | | No log | 2.0 | 12 | 3.1395 | | No log | 3.0 | 18 | 2.6092 | | No log | 4.0 | 24 | 2.5084 | | No log | 5.0 | 30 | 2.5760 | | d9943dc4e76506b5a61a747eae538cb9 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sv', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.2779 - Wer: 0.2525 | 429b33b2b53e73d3a124234afe7594b3 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sv', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3224 | 1.37 | 500 | 3.3354 | 1.0 | | 2.9318 | 2.74 | 1000 | 2.9361 | 1.0000 | | 2.1371 | 4.11 | 1500 | 1.1157 | 0.8359 | | 1.6883 | 5.48 | 2000 | 0.6003 | 0.6314 | | 1.5812 | 6.85 | 2500 | 0.4746 | 0.4725 | | 1.5145 | 8.22 | 3000 | 0.4376 | 0.4736 | | 1.4763 | 9.59 | 3500 | 0.4006 | 0.3863 | | 1.4215 | 10.96 | 4000 | 0.3783 | 0.3629 | | 1.3638 | 12.33 | 4500 | 0.3555 | 0.3425 | | 1.3561 | 13.7 | 5000 | 0.3340 | 0.3228 | | 1.3406 | 15.07 | 5500 | 0.3373 | 0.3295 | | 1.3055 | 16.44 | 6000 | 0.3432 | 0.3210 | | 1.3048 | 17.81 | 6500 | 0.3282 | 0.3118 | | 1.2863 | 19.18 | 7000 | 0.3226 | 0.3018 | | 1.2389 | 20.55 | 7500 | 0.3050 | 0.2986 | | 1.2361 | 21.92 | 8000 | 0.3048 | 0.2980 | | 1.2263 | 23.29 | 8500 | 0.3011 | 0.2977 | | 1.2225 | 24.66 | 9000 | 0.3017 | 0.2959 | | 1.2044 | 26.03 | 9500 | 0.2977 | 0.2782 | | 1.2017 | 27.4 | 10000 | 0.2966 | 0.2781 | | 1.1912 | 28.77 | 10500 | 0.2999 | 0.2786 | | 1.1658 | 30.14 | 11000 | 0.2991 | 0.2757 | | 1.148 | 31.51 | 11500 | 0.2915 | 0.2684 | | 1.1423 | 32.88 | 12000 | 0.2913 | 0.2643 | | 1.123 | 34.25 | 12500 | 0.2777 | 0.2630 | | 1.1297 | 35.62 | 13000 | 0.2873 | 0.2646 | | 1.0987 | 36.98 | 13500 | 0.2829 | 0.2619 | | 1.0873 | 38.36 | 14000 | 0.2864 | 0.2608 | | 1.0848 | 39.73 | 14500 | 0.2827 | 0.2577 | | 1.0628 | 41.1 | 15000 | 0.2896 | 0.2581 | | 1.0815 | 42.47 | 15500 | 0.2814 | 0.2561 | | 1.0587 | 43.83 | 16000 | 0.2738 | 0.2542 | | 1.0709 | 45.21 | 16500 | 0.2785 | 0.2578 | | 1.0512 | 46.57 | 17000 | 0.2793 | 0.2539 | | 1.0396 | 47.94 | 17500 | 0.2788 | 0.2525 | | 1.0481 | 49.31 | 18000 | 0.2777 | 0.2534 | | 21121459e3c8737181dce7e58e412fc0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sv', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id patrickvonplaten/xls-r-300m-sv-cv8 --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id patrickvonplaten/xls-r-300m-sv-cv8 --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` | 1c7b81433c8f15273fa4c4d41e683468 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | ko_core_news_sm Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `ko_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [UD Korean Kaist v2.8](https://github.com/UniversalDependencies/UD_Korean-Kaist) (Choi, Jinho; Han, Na-Rae; Hwang, Jena; Chun, Jayeol)<br />[KLUE v1.1.0](https://github.com/KLUE-benchmark/KLUE) (Sungjoon Park, Jihyung Moon, Sungdong Kim, Won Ik Cho, Jiyoon Han, Jangwon Park, Chisung Song, Junseong Kim, Youngsook Song, Taehwan Oh, Joohong Lee, Juhyun Oh, Sungwon Ryu, Younghoon Jeong, Inkwon Lee, Sangwoo Seo, Dongjun Lee, Hyunwoo Kim, Myeonghwa Lee, Seongbo Jang, Seungwon Do, Sunkyoung Kim, Kyungtae Lim, Jongwon Lee, Kyumin Park, Jamin Shin, Seonghyun Kim, Lucy Park, Alice Oh, Jung-Woo Ha, Kyunghyun Cho) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | | 6c840c7b8b5b3de712b5fde1d260fbaf |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (2028 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `_SP`, `ecs`, `etm`, `f`, `f+f+jcj`, `f+f+jcs`, `f+f+jct`, `f+f+jxt`, `f+jca`, `f+jca+jp+ecc`, `f+jca+jp+ep+ef`, `f+jca+jxc`, `f+jca+jxc+jcm`, `f+jca+jxt`, `f+jcj`, `f+jcm`, `f+jco`, `f+jcs`, `f+jct`, `f+jct+jcm`, `f+jp+ef`, `f+jp+ep+ef`, `f+jp+etm`, `f+jxc`, `f+jxt`, `f+ncn`, `f+ncn+jcm`, `f+ncn+jcs`, `f+ncn+jp+ecc`, `f+ncn+jxt`, `f+ncpa+jcm`, `f+npp+jcs`, `f+nq`, `f+xsn`, `f+xsn+jco`, `f+xsn+jxt`, `ii`, `jca`, `jca+jcm`, `jca+jxc`, `jca+jxt`, `jcc`, `jcj`, `jcm`, `jco`, `jcr`, `jcr+jxc`, `jcs`, `jct`, `jct+jcm`, `jct+jxt`, `jp+ecc`, `jp+ecs`, `jp+ef`, `jp+ef+jcr`, `jp+ef+jcr+jxc`, `jp+ep+ecs`, `jp+ep+ef`, `jp+ep+etm`, `jp+ep+etn`, `jp+etm`, `jp+etn`, `jp+etn+jco`, `jp+etn+jxc`, `jxc`, `jxc+jca`, `jxc+jco`, `jxc+jcs`, `jxt`, `mad`, `mad+jxc`, `mad+jxt`, `mag`, `mag+jca`, `mag+jcm`, `mag+jcs`, `mag+jp+ef+jcr`, `mag+jxc`, `mag+jxc+jxc`, `mag+jxt`, `mag+xsn`, `maj`, `maj+jxc`, `maj+jxt`, `mma`, `mmd`, `nbn`, `nbn+jca`, `nbn+jca+jcj`, `nbn+jca+jcm`, `nbn+jca+jp+ef`, `nbn+jca+jxc`, `nbn+jca+jxt`, `nbn+jcc`, `nbn+jcj`, `nbn+jcm`, `nbn+jco`, `nbn+jcr`, `nbn+jcs`, `nbn+jct`, `nbn+jct+jcm`, `nbn+jct+jxt`, `nbn+jp+ecc`, `nbn+jp+ecs`, `nbn+jp+ecs+jca`, `nbn+jp+ecs+jcm`, `nbn+jp+ecs+jco`, `nbn+jp+ecs+jxc`, `nbn+jp+ecs+jxt`, `nbn+jp+ecx`, `nbn+jp+ef`, `nbn+jp+ef+jca`, `nbn+jp+ef+jco`, `nbn+jp+ef+jcr`, `nbn+jp+ef+jcr+jxc`, `nbn+jp+ef+jcr+jxt`, `nbn+jp+ef+jcs`, `nbn+jp+ef+jxc`, `nbn+jp+ef+jxc+jco`, `nbn+jp+ef+jxf`, `nbn+jp+ef+jxt`, `nbn+jp+ep+ecc`, `nbn+jp+ep+ecs`, `nbn+jp+ep+ecs+jxc`, `nbn+jp+ep+ef`, `nbn+jp+ep+ef+jcr`, `nbn+jp+ep+etm`, `nbn+jp+ep+etn`, `nbn+jp+ep+etn+jco`, `nbn+jp+ep+etn+jcs`, `nbn+jp+etm`, `nbn+jp+etn`, `nbn+jp+etn+jca`, `nbn+jp+etn+jca+jxt`, `nbn+jp+etn+jco`, `nbn+jp+etn+jcs`, `nbn+jp+etn+jxc`, `nbn+jp+etn+jxt`, `nbn+jxc`, `nbn+jxc+jca`, `nbn+jxc+jca+jxc`, `nbn+jxc+jca+jxt`, `nbn+jxc+jcc`, `nbn+jxc+jcm`, `nbn+jxc+jco`, `nbn+jxc+jcs`, `nbn+jxc+jp+ef`, `nbn+jxc+jxc`, `nbn+jxc+jxt`, `nbn+jxt`, `nbn+nbn`, `nbn+nbn+jp+ef`, `nbn+xsm+ecs`, `nbn+xsm+ef`, `nbn+xsm+ep+ef`, `nbn+xsm+ep+ef+jcr`, `nbn+xsm+etm`, `nbn+xsn`, `nbn+xsn+jca`, `nbn+xsn+jca+jp+ef+jcr`, `nbn+xsn+jca+jxc`, `nbn+xsn+jca+jxt`, `nbn+xsn+jcm`, `nbn+xsn+jco`, `nbn+xsn+jcs`, `nbn+xsn+jct`, `nbn+xsn+jp+ecc`, `nbn+xsn+jp+ecs`, `nbn+xsn+jp+ef`, `nbn+xsn+jp+ef+jcr`, `nbn+xsn+jp+ep+ef`, `nbn+xsn+jxc`, `nbn+xsn+jxt`, `nbn+xsv+etm`, `nbu`, `nbu+jca`, `nbu+jca+jxc`, `nbu+jca+jxt`, `nbu+jcc`, `nbu+jcc+jxc`, `nbu+jcj`, `nbu+jcm`, `nbu+jco`, `nbu+jcs`, `nbu+jct`, `nbu+jct+jxc`, `nbu+jp+ecc`, `nbu+jp+ecs`, `nbu+jp+ef`, `nbu+jp+ef+jcr`, `nbu+jp+ef+jxc`, `nbu+jp+ep+ecc`, `nbu+jp+ep+ecs`, `nbu+jp+ep+ef`, `nbu+jp+ep+ef+jcr`, `nbu+jp+ep+etm`, `nbu+jp+ep+etn+jco`, `nbu+jp+etm`, `nbu+jxc`, `nbu+jxc+jca`, `nbu+jxc+jcs`, `nbu+jxc+jp+ef`, `nbu+jxc+jp+ep+ef`, `nbu+jxc+jxt`, `nbu+jxt`, `nbu+ncn`, `nbu+ncn+jca`, `nbu+ncn+jcm`, `nbu+xsn`, `nbu+xsn+jca`, `nbu+xsn+jca+jxc`, `nbu+xsn+jca+jxt`, `nbu+xsn+jcm`, `nbu+xsn+jco`, `nbu+xsn+jcs`, `nbu+xsn+jp+ecs`, `nbu+xsn+jp+ep+ef`, `nbu+xsn+jxc`, `nbu+xsn+jxc+jxt`, `nbu+xsn+jxt`, `nbu+xsv+ecc`, `nbu+xsv+etm`, `ncn`, `ncn+f+ncpa+jco`, `ncn+jca`, `ncn+jca+jca`, `ncn+jca+jcc`, `ncn+jca+jcj`, `ncn+jca+jcm`, `ncn+jca+jcs`, `ncn+jca+jct`, `ncn+jca+jp+ecc`, `ncn+jca+jp+ecs`, `ncn+jca+jp+ef`, `ncn+jca+jp+ep+ef`, `ncn+jca+jp+etm`, `ncn+jca+jp+etn+jxt`, `ncn+jca+jxc`, `ncn+jca+jxc+jcc`, `ncn+jca+jxc+jcm`, `ncn+jca+jxc+jxc`, `ncn+jca+jxc+jxt`, `ncn+jca+jxt`, `ncn+jcc`, `ncn+jcc+jxc`, `ncn+jcj`, `ncn+jcj+jxt`, `ncn+jcm`, `ncn+jco`, `ncn+jcr`, `ncn+jcr+jxc`, `ncn+jcs`, `ncn+jcs+jxt`, `ncn+jct`, `ncn+jct+jcm`, `ncn+jct+jxc`, `ncn+jct+jxt`, `ncn+jcv`, `ncn+jp+ecc`, `ncn+jp+ecc+jct`, `ncn+jp+ecc+jxc`, `ncn+jp+ecs`, `ncn+jp+ecs+jcm`, `ncn+jp+ecs+jco`, `ncn+jp+ecs+jxc`, `ncn+jp+ecs+jxt`, `ncn+jp+ecx`, `ncn+jp+ef`, `ncn+jp+ef+jca`, `ncn+jp+ef+jcm`, `ncn+jp+ef+jco`, `ncn+jp+ef+jcr`, `ncn+jp+ef+jcr+jxc`, `ncn+jp+ef+jcr+jxt`, `ncn+jp+ef+jp+etm`, `ncn+jp+ef+jxc`, `ncn+jp+ef+jxf`, `ncn+jp+ef+jxt`, `ncn+jp+ep+ecc`, `ncn+jp+ep+ecs`, `ncn+jp+ep+ecs+jxc`, `ncn+jp+ep+ecx`, `ncn+jp+ep+ef`, `ncn+jp+ep+ef+jcr`, `ncn+jp+ep+ef+jcr+jxc`, `ncn+jp+ep+ef+jxc`, `ncn+jp+ep+ef+jxf`, `ncn+jp+ep+ef+jxt`, `ncn+jp+ep+ep+etm`, `ncn+jp+ep+etm`, `ncn+jp+ep+etn`, `ncn+jp+ep+etn+jca`, `ncn+jp+ep+etn+jca+jxc`, `ncn+jp+ep+etn+jco`, `ncn+jp+ep+etn+jcs`, `ncn+jp+ep+etn+jxt`, `ncn+jp+etm`, `ncn+jp+etn`, `ncn+jp+etn+jca`, `ncn+jp+etn+jca+jxc`, `ncn+jp+etn+jca+jxt`, `ncn+jp+etn+jco`, `ncn+jp+etn+jcs`, `ncn+jp+etn+jct`, `ncn+jp+etn+jxc`, `ncn+jp+etn+jxt`, `ncn+jxc`, `ncn+jxc+jca`, `ncn+jxc+jca+jxc`, `ncn+jxc+jca+jxt`, `ncn+jxc+jcc`, `ncn+jxc+jcm`, `ncn+jxc+jco`, `ncn+jxc+jcs`, `ncn+jxc+jct+jxt`, `ncn+jxc+jp+ef`, `ncn+jxc+jp+ef+jcr`, `ncn+jxc+jp+ep+ecs`, `ncn+jxc+jp+ep+ef`, `ncn+jxc+jp+etm`, `ncn+jxc+jxc`, `ncn+jxc+jxt`, `ncn+jxt`, `ncn+jxt+jcm`, `ncn+jxt+jxc`, `ncn+nbn`, `ncn+nbn+jca`, `ncn+nbn+jcm`, `ncn+nbn+jcs`, `ncn+nbn+jp+ecc`, `ncn+nbn+jp+ep+ef`, `ncn+nbn+jxc`, `ncn+nbn+jxt`, `ncn+nbu`, `ncn+nbu+jca`, `ncn+nbu+jcm`, `ncn+nbu+jco`, `ncn+nbu+jp+ef`, `ncn+nbu+jxc`, `ncn+nbu+ncn`, `ncn+ncn`, `ncn+ncn+jca`, `ncn+ncn+jca+jcc`, `ncn+ncn+jca+jcm`, `ncn+ncn+jca+jxc`, `ncn+ncn+jca+jxc+jcm`, `ncn+ncn+jca+jxc+jxc`, `ncn+ncn+jca+jxt`, `ncn+ncn+jcc`, `ncn+ncn+jcj`, `ncn+ncn+jcm`, `ncn+ncn+jco`, `ncn+ncn+jcr`, `ncn+ncn+jcs`, `ncn+ncn+jct`, `ncn+ncn+jct+jcm`, `ncn+ncn+jct+jxc`, `ncn+ncn+jct+jxt`, `ncn+ncn+jp+ecc`, `ncn+ncn+jp+ecs`, `ncn+ncn+jp+ef`, `ncn+ncn+jp+ef+jcm`, `ncn+ncn+jp+ef+jcr`, `ncn+ncn+jp+ef+jcs`, `ncn+ncn+jp+ep+ecc`, `ncn+ncn+jp+ep+ecs`, `ncn+ncn+jp+ep+ef`, `ncn+ncn+jp+ep+ef+jcr`, `ncn+ncn+jp+ep+ep+etm`, `ncn+ncn+jp+ep+etm`, `ncn+ncn+jp+ep+etn`, `ncn+ncn+jp+etm`, `ncn+ncn+jp+etn`, `ncn+ncn+jp+etn+jca`, `ncn+ncn+jp+etn+jco`, `ncn+ncn+jp+etn+jxc`, `ncn+ncn+jxc`, `ncn+ncn+jxc+jca`, `ncn+ncn+jxc+jcc`, `ncn+ncn+jxc+jcm`, `ncn+ncn+jxc+jco`, `ncn+ncn+jxc+jcs`, `ncn+ncn+jxc+jxc`, `ncn+ncn+jxt`, `ncn+ncn+nbn`, `ncn+ncn+ncn`, `ncn+ncn+ncn+jca`, `ncn+ncn+ncn+jca+jcm`, `ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+jcj`, `ncn+ncn+ncn+jcm`, `ncn+ncn+ncn+jco`, `ncn+ncn+ncn+jcs`, `ncn+ncn+ncn+jct+jxt`, `ncn+ncn+ncn+jp+etn+jxc`, `ncn+ncn+ncn+jxt`, `ncn+ncn+ncn+ncn+jca`, `ncn+ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+ncn+jco`, `ncn+ncn+ncn+xsn+jp+etm`, `ncn+ncn+ncpa`, `ncn+ncn+ncpa+jca`, `ncn+ncn+ncpa+jcm`, `ncn+ncn+ncpa+jco`, `ncn+ncn+ncpa+jcs`, `ncn+ncn+ncpa+jxc`, `ncn+ncn+ncpa+jxt`, `ncn+ncn+ncpa+ncn`, `ncn+ncn+ncpa+ncn+jca`, `ncn+ncn+ncpa+ncn+jcj`, `ncn+ncn+ncpa+ncn+jcm`, `ncn+ncn+ncpa+ncn+jxt`, `ncn+ncn+xsn`, `ncn+ncn+xsn+jca`, `ncn+ncn+xsn+jca+jxt`, `ncn+ncn+xsn+jcj`, `ncn+ncn+xsn+jcm`, `ncn+ncn+xsn+jco`, `ncn+ncn+xsn+jcs`, `ncn+ncn+xsn+jct`, `ncn+ncn+xsn+jp+ecs`, `ncn+ncn+xsn+jp+ep+ef`, `ncn+ncn+xsn+jp+etm`, `ncn+ncn+xsn+jxc`, `ncn+ncn+xsn+jxc+jcs`, `ncn+ncn+xsn+jxt`, `ncn+ncn+xsv+ecc`, `ncn+ncn+xsv+etm`, `ncn+ncpa`, `ncn+ncpa+jca`, `ncn+ncpa+jca+jcm`, `ncn+ncpa+jca+jxc`, `ncn+ncpa+jca+jxt`, `ncn+ncpa+jcc`, `ncn+ncpa+jcj`, `ncn+ncpa+jcm`, `ncn+ncpa+jco`, `ncn+ncpa+jcr`, `ncn+ncpa+jcs`, `ncn+ncpa+jct`, `ncn+ncpa+jct+jcm`, `ncn+ncpa+jct+jxt`, `ncn+ncpa+jp+ecc`, `ncn+ncpa+jp+ecc+jxc`, `ncn+ncpa+jp+ecs`, `ncn+ncpa+jp+ecs+jxc`, `ncn+ncpa+jp+ef`, `ncn+ncpa+jp+ef+jcr`, `ncn+ncpa+jp+ef+jcr+jxc`, `ncn+ncpa+jp+ep+ef`, `ncn+ncpa+jp+ep+etm`, `ncn+ncpa+jp+ep+etn`, `ncn+ncpa+jp+etm`, `ncn+ncpa+jxc`, `ncn+ncpa+jxc+jca+jxc`, `ncn+ncpa+jxc+jco`, `ncn+ncpa+jxc+jcs`, `ncn+ncpa+jxt`, `ncn+ncpa+nbn+jcs`, `ncn+ncpa+ncn`, `ncn+ncpa+ncn+jca`, `ncn+ncpa+ncn+jca+jcm`, `ncn+ncpa+ncn+jca+jxc`, `ncn+ncpa+ncn+jca+jxt`, `ncn+ncpa+ncn+jcj`, `ncn+ncpa+ncn+jcm`, `ncn+ncpa+ncn+jco`, `ncn+ncpa+ncn+jcs`, `ncn+ncpa+ncn+jct`, `ncn+ncpa+ncn+jct+jcm`, `ncn+ncpa+ncn+jp+ef+jcr`, `ncn+ncpa+ncn+jp+ep+etm`, `ncn+ncpa+ncn+jxc`, `ncn+ncpa+ncn+jxt`, `ncn+ncpa+ncn+xsn+jcm`, `ncn+ncpa+ncn+xsn+jxt`, `ncn+ncpa+ncpa`, `ncn+ncpa+ncpa+jca`, `ncn+ncpa+ncpa+jcj`, `ncn+ncpa+ncpa+jcm`, `ncn+ncpa+ncpa+jco`, `ncn+ncpa+ncpa+jcs`, `ncn+ncpa+ncpa+jp+ep+ef`, `ncn+ncpa+ncpa+jxt`, `ncn+ncpa+ncpa+ncn`, `ncn+ncpa+xsn`, `ncn+ncpa+xsn+jcm`, `ncn+ncpa+xsn+jco`, `ncn+ncpa+xsn+jcs`, `ncn+ncpa+xsn+jp+ecc`, `ncn+ncpa+xsn+jp+etm`, `ncn+ncpa+xsn+jxt`, `ncn+ncpa+xsv+ecc`, `ncn+ncpa+xsv+ecs`, `ncn+ncpa+xsv+ecx`, `ncn+ncpa+xsv+ecx+px+etm`, `ncn+ncpa+xsv+ef`, `ncn+ncpa+xsv+ef+jcm`, `ncn+ncpa+xsv+ef+jcr`, `ncn+ncpa+xsv+etm`, _(truncated: full list in pipeline meta)_ | | **`morphologizer`** | `POS=CCONJ`, `POS=ADV`, `POS=SCONJ`, `POS=DET`, `POS=NOUN`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=SPACE`, `POS=AUX`, `POS=PRON`, `POS=PROPN`, `POS=NUM`, `POS=INTJ`, `POS=PART`, `POS=X`, `POS=ADP`, `POS=SYM` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct`, `xcomp` | | **`ner`** | `DT`, `LC`, `OG`, `PS`, `QT`, `TI` | </details> | b0526e4e0d7483c7c1c90282db0fce46 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 100.00 | | `TOKEN_R` | 100.00 | | `TOKEN_F` | 100.00 | | `TAG_ACC` | 73.06 | | `POS_ACC` | 85.82 | | `SENTS_P` | 99.90 | | `SENTS_R` | 99.95 | | `SENTS_F` | 99.93 | | `DEP_UAS` | 73.61 | | `DEP_LAS` | 65.59 | | `LEMMA_ACC` | 83.57 | | `ENTS_P` | 77.04 | | `ENTS_R` | 66.03 | | `ENTS_F` | 71.11 | | 6af992164bb6c9280f22f33b58cde8ed |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-squad-plain_text-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.3247 | f85a29d410c1e814854732b54d767e78 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.5181 | 0.4 | 500 | 7.5716 | | 6.4657 | 0.8 | 1000 | 7.5778 | | 6.2336 | 1.2 | 1500 | 7.4653 | | 6.0699 | 1.6 | 2000 | 7.4193 | | 5.946 | 2.0 | 2500 | 7.2908 | | 5.7981 | 2.4 | 3000 | 7.2710 | | 5.8332 | 2.8 | 3500 | 7.3876 | | 5.772 | 3.2 | 4000 | 7.3050 | | 5.6513 | 3.6 | 4500 | 7.3247 | | 86f006d4673f556cbfcd16d32c950ec5 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-dpv-finetuned-WITH-AUGMENTATION-LOWER-LR This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5717 - Wer: 34.5241 | 6407580f937d1347dcf3e5e68d72435c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 4 - mixed_precision_training: Native AMP | b022967b47cf3a5a39edf4b6693000f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6221 | 0.62 | 1000 | 0.5345 | 35.9711 | | 0.4318 | 1.25 | 2000 | 0.5271 | 34.9537 | | 0.3859 | 1.87 | 3000 | 0.5338 | 34.3658 | | 0.3005 | 2.49 | 4000 | 0.5532 | 34.8858 | | 0.2444 | 3.12 | 5000 | 0.5628 | 33.7102 | | 0.315 | 3.74 | 6000 | 0.5717 | 34.5241 | | edfced2838ef8fe976e794754601d664 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation'] | false | IT5 Small for Question Generation 💭 🇮🇹 This repository contains the checkpoint for the [IT5 Small](https://huggingface.co/gsarti/it5-small) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. | 19c4fcc6480356d57049f6f417329ccd |
apache-2.0 | ['italian', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines qg = pipeline("text2text-generation", model='it5/it5-small-question-generation') qg("Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una "grande pestilenza nell\' aria". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola "peste" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia") >>> [{"generated_text": "Per chi è stato redatto il referto medico?"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/it5-small-question-generation") model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-small-question-generation") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ``` | fa9b0fca5aaee2b82a7505570524bcbf |
apache-2.0 | [] | false | Perceiver IO for language Perceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in [BERT](https://arxiv.org/abs/1810.04805) using a large text corpus obtained by combining [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/perceiver). Disclaimer: The team releasing Perceiver IO did not write a model card for this model so this model card has been written by the Hugging Face team. | 3ada85f3730b8e8807dcecc56334db73 |
apache-2.0 | [] | false | Model description Perceiver IO is a transformer encoder model that can be applied on any modality (text, images, audio, video, ...). The core idea is to employ the self-attention mechanism on a not-too-large set of latent vectors (e.g. 256 or 512), and only use the inputs to perform cross-attention with the latents. This allows for the time and memory requirements of the self-attention mechanism to not depend on the size of the inputs. To decode, the authors employ so-called decoder queries, which allow to flexibly decode the final hidden states of the latents to produce outputs of arbitrary size and semantics. For masked language modeling, the output is a tensor containing the prediction scores of the language modeling head, of shape (batch_size, seq_length, vocab_size). <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/perceiver_architecture.jpg" alt="drawing" width="600"/> <small> Perceiver IO architecture.</small> As the time and memory requirements of the self-attention mechanism don't depend on the size of the inputs, the Perceiver IO authors train the model directly on raw UTF-8 bytes, rather than on subwords as is done in models like BERT, RoBERTa and GPT-2. This has many benefits: one doesn't need to train a tokenizer before training the model, one doesn't need to maintain a (fixed) vocabulary file, and this also doesn't hurt model performance as shown by [Bostrom et al., 2020](https://arxiv.org/abs/2004.03720). By pre-training the model, it learns an inner representation of language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the Perceiver model as inputs. | 7aa5c3bcc898ec241f2553253f4adfad |
apache-2.0 | [] | false | Intended uses & limitations You can use the raw model for masked language modeling, but the model is intended to be fine-tuned on a labeled dataset. See the [model hub](https://huggingface.co/models?search=deepmind/perceiver) to look for fine-tuned versions on a task that interests you. | 2b017f03585a935676070a0d0ff9fb24 |
apache-2.0 | [] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import PerceiverTokenizer, PerceiverForMaskedLM tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver") model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver") text = "This is an incomplete sentence where some words are missing." | 46362c4854509853672a399e14581234 |
apache-2.0 | [] | false | mask " missing.". Note that the model performs much better if the masked span starts with a space. encoding.input_ids[0, 52:61] = tokenizer.mask_token_id inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device) | c35be4cf331f8cf14dac4d9cbebe5a06 |
apache-2.0 | [] | false | forward pass outputs = model(inputs=inputs, attention_mask=input_mask) logits = outputs.logits masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1) print(tokenizer.decode(masked_tokens_predictions)) >>> should print " missing." ``` | 51b8edd23806d768aa1a21fc79d0cd83 |
apache-2.0 | [] | false | Training data This model was pretrained on a combination of [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). 70% of the training tokens were sampled from the C4 dataset and the remaining 30% from Wikipedia. The authors concatenate 10 documents before splitting into crops to reduce wasteful computation on padding tokens. | c318950a456b5c14f1bd880b59326fd2 |
apache-2.0 | ['exbert', 'security', 'cybersecurity', 'cyber security', 'threat hunting', 'threat intelligence'] | false | SecRoBERTa This is the pretrained model presented in [SecBERT: A Pretrained Language Model for Cyber Security Text](https://github.com/jackaduma/SecBERT/), which is a SecRoBERTa model trained on cyber security text. The training corpus was papers taken from * [APTnotes](https://github.com/kbandla/APTnotes) * [Stucco-Data: Cyber security data sources](https://stucco.github.io/data/) * [CASIE: Extracting Cybersecurity Event Information from Text](https://ebiquity.umbc.edu/_file_directory_/papers/943.pdf) * [SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP)](https://competitions.codalab.org/competitions/17262). SecRoBERTa has its own wordpiece vocabulary (secvocab) that's built to best match the training corpus. We trained [SecBERT](https://huggingface.co/jackaduma/SecBERT) and [SecRoBERTa](https://huggingface.co/jackaduma/SecRoBERTa) versions. Available models include: * [`SecBERT`](https://huggingface.co/jackaduma/SecBERT) * [`SecRoBERTa`](https://huggingface.co/jackaduma/SecRoBERTa) --- | d618d068391ba0129767813a51bdbe53 |
apache-2.0 | ['exbert', 'security', 'cybersecurity', 'cyber security', 'threat hunting', 'threat intelligence'] | false | **Fill Mask** We proposed to build language model which work on cyber security text, as result, it can improve downstream tasks (NER, Text Classification, Semantic Understand, Q&A) in Cyber Security Domain. First, as below shows Fill-Mask pipeline in [Google Bert](), [AllenAI SciBert](https://github.com/allenai/scibert) and our [SecBERT](https://github.com/jackaduma/SecBERT) . <!-- <img src="./fill-mask-result.png" width="150%" height="150%"> -->  --- The original repo can be found [here](https://github.com/jackaduma/SecBERT). | 593b7bb2e53ba8432a6331abeed33499 |
apache-2.0 | ['translation'] | false | opus-mt-en-lg * source languages: en * target languages: lg * OPUS readme: [en-lg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-lg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-lg/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lg/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lg/opus-2020-01-08.eval.txt) | 6dd7bcc2d299cebc8c7b2edf8b10f37a |
apache-2.0 | ['generated_from_keras_callback'] | false | Imene/vit-base-patch16-384-wi3 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2020 - Train Accuracy: 0.9984 - Train Top-3-accuracy: 0.9997 - Validation Loss: 1.4297 - Validation Accuracy: 0.6195 - Validation Top-3-accuracy: 0.8298 - Epoch: 11 | 76d697f6f701dca5c1cefef20a3e4995 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | 806a286eb1631eecaec77dbf017e64a0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 3.6575 | 0.0902 | 0.1945 | 3.1772 | 0.2028 | 0.3980 | 0 | | 2.5870 | 0.3473 | 0.6048 | 2.3845 | 0.3717 | 0.6208 | 1 | | 1.8813 | 0.5553 | 0.7895 | 2.0262 | 0.4431 | 0.7196 | 2 | | 1.4326 | 0.6815 | 0.8754 | 1.8856 | 0.4793 | 0.7384 | 3 | | 1.0572 | 0.7989 | 0.9439 | 1.6570 | 0.5369 | 0.7960 | 4 | | 0.7740 | 0.8838 | 0.9749 | 1.6103 | 0.5557 | 0.7960 | 5 | | 0.5593 | 0.9417 | 0.9900 | 1.5303 | 0.5695 | 0.8173 | 6 | | 0.4151 | 0.9709 | 0.9975 | 1.4939 | 0.5795 | 0.8185 | 7 | | 0.3176 | 0.9884 | 0.9978 | 1.4553 | 0.5832 | 0.8248 | 8 | | 0.2582 | 0.9950 | 0.9991 | 1.4500 | 0.6020 | 0.8248 | 9 | | 0.2222 | 0.9978 | 0.9994 | 1.4315 | 0.6108 | 0.8310 | 10 | | 0.2020 | 0.9984 | 0.9997 | 1.4297 | 0.6195 | 0.8298 | 11 | | dffd60b98004d90e89cab0812ba6bb39 |
apache-2.0 | ['generated_from_trainer'] | false | ner_ANAT_DISO This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0746 - Anat Precision: 0.6512 - Anat Recall: 0.6573 - Anat F1: 0.6542 - Anat Number: 534 - Diso Precision: 0.8727 - Diso Recall: 0.8844 - Diso F1: 0.8785 - Diso Number: 2915 - Overall Precision: 0.8385 - Overall Recall: 0.8492 - Overall F1: 0.8438 - Overall Accuracy: 0.9838 | a08f461707017774d79d37dc6550c3ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.0625 | 1.0 | 1682 | 0.0591 | 0.5407 | 0.6723 | 0.5993 | 534 | 0.8516 | 0.8624 | 0.8570 | 2915 | 0.7945 | 0.8330 | 0.8133 | 0.9808 | | 0.0397 | 2.0 | 3364 | 0.0633 | 0.6237 | 0.6798 | 0.6505 | 534 | 0.8576 | 0.8820 | 0.8696 | 2915 | 0.8196 | 0.8507 | 0.8348 | 0.9826 | | 0.0181 | 3.0 | 5046 | 0.0698 | 0.6452 | 0.6948 | 0.6691 | 534 | 0.8670 | 0.8878 | 0.8773 | 2915 | 0.8312 | 0.8579 | 0.8443 | 0.9833 | | 0.0121 | 4.0 | 6728 | 0.0746 | 0.6512 | 0.6573 | 0.6542 | 534 | 0.8727 | 0.8844 | 0.8785 | 2915 | 0.8385 | 0.8492 | 0.8438 | 0.9838 | | 94f56c71d774a5cca8ee95e407e0a06b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0578 - Precision: 0.9189 - Recall: 0.9357 - F1: 0.9272 - Accuracy: 0.9831 | f7cf12bd54512f832e568c6bb9d45953 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0754 | 1.0 | 1756 | 0.0578 | 0.9189 | 0.9357 | 0.9272 | 0.9831 | | e9a91e40de2ab24fcf1d4c3853e21398 |
cc-by-4.0 | ['bert'] | false | bert-fc-medium A medium-size BERT Language Model with a **first character** prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclanthology.org/2022.acl-short.16/) | 5b2178638a9b871d45d637476af509cf |
apache-2.0 | ['translation'] | false | opus-mt-uk-sv * source languages: uk * target languages: sv * OPUS readme: [uk-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/uk-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/uk-sv/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/uk-sv/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/uk-sv/opus-2020-01-16.eval.txt) | 7b0d1455eddc27ad44b58a1e5d5dbc0b |
other | ['vision', 'image-segmentation'] | false | SegFormer (b4-sized) model fine-tuned on CityScapes SegFormer model fine-tuned on CityScapes at resolution 512x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. | 5ed56bd1bc8ed09c6f0c28c77fc76766 |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-512-1024") model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-512-1024") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits | dd7f865e13fefeb2bb63e70579c4e6f3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-misogyny-sexism-4tweets-3e-05-0.01 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1537 - Accuracy: 0.6647 - F1: 0.6788 - Precision: 0.6076 - Recall: 0.7691 - Mae: 0.3353 - Tn: 309 - Fp: 228 - Fn: 106 - Tp: 353 | 692f16b02bf43ca79923fde332bf3884 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:| | 0.4449 | 1.0 | 1655 | 0.6853 | 0.5944 | 0.6799 | 0.5342 | 0.9346 | 0.4056 | 163 | 374 | 30 | 429 | | 0.3732 | 2.0 | 3310 | 0.7372 | 0.6416 | 0.6238 | 0.6041 | 0.6449 | 0.3584 | 343 | 194 | 163 | 296 | | 0.2962 | 3.0 | 4965 | 0.7860 | 0.6717 | 0.6714 | 0.6231 | 0.7277 | 0.3283 | 335 | 202 | 125 | 334 | | 0.2235 | 4.0 | 6620 | 1.1537 | 0.6647 | 0.6788 | 0.6076 | 0.7691 | 0.3353 | 309 | 228 | 106 | 353 | | 981580a1b69c07d026971a566979a63b |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-small-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-luw-upos), dependency-parsing, and so on. | d99a4ea934430624864515ebbc4d0046 |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora") ``` | cd480c71b6973e6188b5ca29a6e53e1a |
mit | ['question generation'] | false | german-qg-t5-e2e-quad (Work in progress) This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of [valhalla/t5-base-e2e-qg](https://huggingface.co/valhalla/t5-base-e2e-qg) on the [GermanQuAD dataset from deepset](https://huggingface.co/datasets/deepset/germanquad). | 041801ca2c97c5867ced5bddb9bd3574 |
mit | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 7fecca0e15dfb7fbf0d8d6c48d7a1a45 |
mit | [] | false | Rishusei style on Stable Diffusion This is the `<crishusei-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:     | 88d4917332765a286d3e0e1a16d2a03d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.food_service_positive.sa.5-class.seed_43 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.7961 - Accuracy: 0.6569 - Macro-f1: 0.6291 - Weighted-macro-f1: 0.6459 | 780b30462e77bed8fa17bc6886c1caa7 |
apache-2.0 | [] | false | PaddlePaddle/uie-m-large Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE. UIE Paper: https://arxiv.org/abs/2203.12277 PaddleNLP released UIE model series for Information Extraction of texts and multi-modal documents which use the ERNIE 3.0 models as the pre-trained language models and were finetuned on a large amount of information extraction data.  | bd9248804d8351f46240e48320681d2d |
apache-2.0 | ['generated_from_trainer'] | false | model_syllable_onSet3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1590 - 0 Precision: 0.9688 - 0 Recall: 1.0 - 0 F1-score: 0.9841 - 0 Support: 31 - 1 Precision: 1.0 - 1 Recall: 1.0 - 1 F1-score: 1.0 - 1 Support: 25 - 2 Precision: 1.0 - 2 Recall: 0.9474 - 2 F1-score: 0.9730 - 2 Support: 19 - 3 Precision: 0.9545 - 3 Recall: 0.9545 - 3 F1-score: 0.9545 - 3 Support: 22 - Accuracy: 0.9794 - Macro avg Precision: 0.9808 - Macro avg Recall: 0.9755 - Macro avg F1-score: 0.9779 - Macro avg Support: 97 - Weighted avg Precision: 0.9797 - Weighted avg Recall: 0.9794 - Weighted avg F1-score: 0.9793 - Weighted avg Support: 97 - Wer: 0.2202 - Mtrix: [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]] | d0307700d0faa9fbb0fd6923468f2c74 |
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