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microsoft/unispeech-sat-large-sv
microsoft
2021-12-17T18:13:15Z
240
4
transformers
[ "transformers", "pytorch", "unispeech-sat", "audio-xvector", "speech", "en", "arxiv:1912.07875", "arxiv:2106.06909", "arxiv:2101.00390", "arxiv:2110.05752", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: - en datasets: tags: - speech --- # UniSpeech-SAT-Large for Speaker Verification [Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre-trained on: - 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875) - 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909) - 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390) [Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu **Abstract** *Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..* The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT. # Fine-tuning details The model is fine-tuned on the [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) using an X-Vector head with an Additive Margin Softmax loss [X-Vectors: Robust DNN Embeddings for Speaker Recognition](https://www.danielpovey.com/files/2018_icassp_xvectors.pdf) # Usage ## Speaker Verification ```python from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForXVector from datasets import load_dataset import torch dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-large-sv') model = UniSpeechSatForXVector.from_pretrained('microsoft/unispeech-sat-large-sv') # audio files are decoded on the fly inputs = feature_extractor(dataset[:2]["audio"]["array"], return_tensors="pt") embeddings = model(**inputs).embeddings embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu() # the resulting embeddings can be used for cosine similarity-based retrieval cosine_sim = torch.nn.CosineSimilarity(dim=-1) similarity = cosine_sim(embeddings[0], embeddings[1]) threshold = 0.89 # the optimal threshold is dataset-dependent if similarity < threshold: print("Speakers are not the same!") ``` # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/UniSpeechSAT.png)
microsoft/unispeech-sat-base-sv
microsoft
2021-12-17T18:11:05Z
200
0
transformers
[ "transformers", "pytorch", "unispeech-sat", "audio-xvector", "speech", "en", "dataset:librispeech_asr", "arxiv:2110.05752", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: - en datasets: - librispeech_asr tags: - speech --- # UniSpeech-SAT-Base for Speaker Verification [Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre-trained on: - 960 hours of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) [Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu **Abstract** *Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..* The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT. # Fine-tuning details The model is fine-tuned on the [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) using an X-Vector head with an Additive Margin Softmax loss [X-Vectors: Robust DNN Embeddings for Speaker Recognition](https://www.danielpovey.com/files/2018_icassp_xvectors.pdf) # Usage ## Speaker Verification ```python from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForXVector from datasets import load_dataset import torch dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-base-sv') model = UniSpeechSatForXVector.from_pretrained('microsoft/unispeech-sat-base-sv') # audio files are decoded on the fly inputs = feature_extractor(dataset[:2]["audio"]["array"], return_tensors="pt") embeddings = model(**inputs).embeddings embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu() # the resulting embeddings can be used for cosine similarity-based retrieval cosine_sim = torch.nn.CosineSimilarity(dim=-1) similarity = cosine_sim(embeddings[0], embeddings[1]) threshold = 0.86 # the optimal threshold is dataset-dependent if similarity < threshold: print("Speakers are not the same!") ``` # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/UniSpeechSAT.png)
butchland/bert-finetuned-ner
butchland
2021-12-17T15:53:25Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9389679126695336 - name: Recall type: recall value: 0.9554022214742511 - name: F1 type: f1 value: 0.9471137804471137 - name: Accuracy type: accuracy value: 0.9873138282215812 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Precision: 0.9390 - Recall: 0.9554 - F1: 0.9471 - Accuracy: 0.9873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0877 | 1.0 | 1756 | 0.0662 | 0.9081 | 0.9344 | 0.9210 | 0.9827 | | 0.0376 | 2.0 | 3512 | 0.0599 | 0.9362 | 0.9502 | 0.9431 | 0.9862 | | 0.0209 | 3.0 | 5268 | 0.0586 | 0.9390 | 0.9554 | 0.9471 | 0.9873 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
osanseviero/test123
osanseviero
2021-12-17T15:42:32Z
0
0
spacy
[ "spacy", "token-classification", "de", "license:cc-by-sa-4.0", "model-index", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- tags: - spacy - token-classification language: - de license: cc-by-sa-4.0 model-index: - name: de_udv25_germanhdt_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9783706437 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9782287343 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.7811165904 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9204479606 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9728029281 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.9588036494 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.99750025 --- UD v2.5 benchmarking pipeline for UD_German-HDT | Feature | Description | | --- | --- | | **Name** | `de_udv25_germanhdt_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (62832 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`, `FM`, `ITJ`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NN`, `PDAT`, `PDS`, `PIAT`, `PIDAT`, `PIS`, `PPER`, `PPOSAT`, `PPOSS`, `PRELAT`, `PRELS`, `PRF`, `PROAV`, `PTKA`, `PTKANT`, `PTKNEG`, `PTKVZ`, `PTKZU`, `PWAT`, `PWAV`, `PWS`, `TRUNC`, `VAFIN`, `VAIMP`, `VAINF`, `VAPP`, `VMFIN`, `VMINF`, `VMPP`, `VVFIN`, `VVIMP`, `VVINF`, `VVIZU`, `VVPP`, `XY` | | **`morphologizer`** | `AdpType=Prep\|Case=Dat\|POS=ADP`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Foreign=Yes\|POS=X\|Person=3`, `POS=PUNCT\|PunctType=Comm`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `AdpType=Prep\|POS=ADP`, `Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `POS=CCONJ`, `POS=PUNCT\|PunctType=Peri`, `NumType=Card\|Number=Plur\|POS=NUM\|Person=3`, `Gender=Fem\|Number=Plur\|POS=NOUN\|Person=3`, `AdpType=Prep\|Case=Dat\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `POS=PUNCT\|PunctType=Brck`, `POS=PROPN\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=ADV`, `POS=SCONJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|VerbForm=Inf`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Sing\|POS=PROPN\|Person=3`, `Degree=Cmp\|POS=ADJ\|Variant=Short`, `POS=ADP\|PartType=Vbp`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART\|Polarity=Neg`, `Degree=Cmp\|POS=ADV`, `ConjType=Comp\|POS=CCONJ`, `Degree=Pos\|POS=ADJ\|Variant=Short`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Aspect=Perf\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3`, `Degree=Sup\|POS=ADJ\|Variant=Short`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Hyph=Yes\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `POS=PART\|PartType=Inf`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=NOUN\|Person=3`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=AUX\|VerbForm=Inf`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Inf\|VerbType=Mod`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|Case=Dat\|Gender=Fem\|POS=ADP\|PronType=Art`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `POS=ADJ`, `Degree=Cmp\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Art`, `POS=ADV\|PronType=Int`, `Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Degree=Cmp\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `AdpType=Post\|Case=Dat\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|POS=AUX\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Number=Plur\|POS=NOUN\|Person=3`, `Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Sup\|Number=Plur\|POS=ADJ\|Person=3`, `Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Number=Plur\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `AdpType=Prep\|Case=Acc\|Gender=Neut\|POS=ADP\|PronType=Art`, `Case=Gen\|Number=Sing\|POS=PROPN\|Person=3`, `Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=3`, `POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Dat\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `POS=ADJ\|Person=3`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `AdpType=Circ\|POS=ADP`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|Case=Nom\|POS=ADP`, `Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Foreign=Yes\|POS=X`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|POS=PROPN\|Person=3`, `Case=Dat\|Number=Plur\|POS=DET\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=ADJ\|Person=3`, `POS=DET`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=X`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `AdpType=Post\|Case=Acc\|POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Degree=Sup\|Number=Plur\|POS=ADJ`, `POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=3`, `NumType=Card\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Degree=Pos\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Degree=Sup\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `POS=ADJ\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Foreign=Yes\|Number=Sing\|POS=X`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|POS=AUX\|VerbForm=Part\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Gender=Masc\|POS=NOUN\|Person=3`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=INTJ\|PartType=Res`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Foreign=Yes\|Gender=Neut\|Number=Sing\|POS=X\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Int`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Gender=Neut\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN\|Person=3`, `Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Number=Plur\|POS=DET\|Person=3`, `Case=Nom\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Hyph=Yes\|Number=Plur\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|POS=PROPN\|Person=3`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Pos\|Number=Sing\|POS=ADJ\|Person=3`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|POS=PRON\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Degree=Sup\|Number=Plur\|POS=ADJ`, `POS=PRON\|PronType=Int`, `Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Hyph=Yes\|POS=NOUN\|Person=3`, `Degree=Pos\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|NumType=Card\|Number=Plur\|POS=NUM\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|POS=SCONJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=DET\|Person=3\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `AdpType=Post\|Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Ind,Neg,Tot`, `Degree=Pos\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `AdpType=Prep\|Case=Acc\|Gender=Fem\|POS=ADP\|PronType=Art`, `Degree=Pos\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|POS=PRON\|PronType=Rel`, `Case=Acc\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `AdpType=Prep\|Case=Dat\|Gender=Neut\|POS=ADP\|PronType=Art`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Dat\|POS=NOUN\|Person=3`, `Degree=Pos\|POS=VERB\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Variant=Short`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=SCONJ\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|POS=DET\|PronType=Art`, `Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|POS=ADP\|PronType=Art`, `Number=Sing\|POS=PRON\|PronType=Ind,Neg,Tot`, `Degree=Sup\|Number=Plur\|POS=DET\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=NOUN\|Person=3`, `AdpType=Prep\|Case=Dat\|Gender=Masc\|POS=ADP\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Degree=Pos\|Gender=Neut\|POS=ADJ`, `Gender=Fem\|POS=ADJ`, `Degree=Pos\|Gender=Fem\|POS=ADJ`, `Gender=Masc\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|VerbType=Mod`, `POS=DET\|Person=3`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `expl:pv`, `flat`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `reparandum`, `vocative`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `0`, `2`, `4`, `6`, `8`, `12`, `14`, `16`, `19`, `22`, `26`, `28`, `30`, `32`, `34`, `38`, `41`, `43`, `45`, `47`, `49`, `53`, `55`, `57`, `58`, `60`, `62`, `65`, `67`, `68`, `69`, `71`, `73`, `75`, `76`, `79`, `81`, `83`, `85`, `86`, `89`, `92`, `94`, `96`, `98`, `100`, `103`, `105`, `106`, `108`, `111`, `113`, `116`, `119`, `122`, `124`, `126`, `129`, `131`, `133`, `136`, `138`, `141`, `143`, `146`, `148`, `151`, `154`, `156`, `158`, `161`, `163`, `165`, `167`, `170`, `173`, `175`, `176`, `178`, `180`, `182`, `183`, `185`, `187`, `189`, `192`, `193`, `195`, `197`, `199`, `202`, `205`, `207`, `209`, `210`, `132`, `212`, `214`, `216`, `218`, `220`, `226`, `229`, `231`, `234`, `236`, `238`, `239`, `240`, `244`, `246`, `248`, `250`, `253`, `257`, `259`, `262`, `263`, `265`, `267`, `269`, `271`, `275`, `277`, `279`, `283`, `285`, `288`, `290`, `292`, `295`, `297`, `299`, `301`, `303`, `307`, `308`, `310`, `311`, `313`, `314`, `316`, `317`, `319`, `321`, `322`, `324`, `325`, `327`, `329`, `331`, `333`, `334`, `337`, `339`, `341`, `343`, `345`, `348`, `349`, `351`, `353`, `355`, `357`, `361`, `363`, `365`, `366`, `368`, `371`, `372`, `373`, `375`, `376`, `378`, `380`, `382`, `383`, `385`, `387`, `389`, `391`, `393`, `395`, `396`, `398`, `399`, `401`, `403`, `405`, `406`, `409`, `412`, `413`, `415`, `417`, `419`, `420`, `421`, `423`, `425`, `427`, `429`, `431`, `433`, `435`, `437`, `439`, `441`, `443`, `448`, `450`, `452`, `454`, `456`, `457`, `459`, `461`, `463`, `465`, `466`, `468`, `470`, `472`, `474`, `476`, `478`, `480`, `482`, `485`, `487`, `489`, `492`, `494`, `495`, `497`, `499`, `500`, `502`, `504`, `506`, `508`, `509`, `510`, `512`, `513`, `516`, `518`, `519`, `521`, `522`, `523`, `525`, `527`, `528`, `529`, `530`, `532`, `534`, `536`, `537`, `544`, `545`, `547`, `549`, `554`, `555`, `556`, `558`, `560`, `562`, `564`, `565`, `567`, `568`, `570`, `572`, `574`, `576`, `577`, `579`, `580`, `581`, `583`, `585`, `587`, `589`, `591`, `592`, `594`, `596`, `599`, `601`, `604`, `608`, `610`, `612`, `614`, `616`, `618`, `620`, `622`, `624`, `625`, `627`, `628`, `630`, `632`, `634`, `635`, `638`, `640`, `642`, `644`, `646`, `647`, `649`, `651`, `656`, `658`, `660`, `661`, `663`, `665`, `667`, `669`, `671`, `256`, `673`, `675`, `677`, `679`, `680`, `682`, `684`, `686`, `688`, `689`, `690`, `692`, `693`, `695`, `697`, `699`, `701`, `702`, `704`, `706`, `708`, `710`, `712`, `714`, `716`, `717`, `719`, `722`, `724`, `726`, `728`, `731`, `733`, `734`, `736`, `738`, `740`, `741`, `744`, `745`, `746`, `748`, `750`, `753`, `754`, `757`, `759`, `760`, `762`, `764`, `766`, `768`, `770`, `771`, `773`, `776`, `778`, `780`, `782`, `784`, `786`, `788`, `789`, `791`, `793`, `795`, `797`, `799`, `801`, `803`, `804`, `806`, `809`, `811`, `812`, `813`, `814`, `815`, `817`, `820`, `821`, `823`, `824`, `827`, `828`, `830`, `833`, `835`, `836`, `843`, `845`, `847`, `849`, `852`, `854`, `858`, `860`, `862`, `864`, `866`, `868`, `870`, `872`, `874`, `876`, `878`, `880`, `882`, `884`, `886`, `888`, `890`, `892`, `895`, `897`, `899`, `901`, `903`, `908`, `911`, `914`, `916`, `918`, `920`, `922`, `924`, `926`, `607`, `928`, `930`, `931`, `932`, `934`, `935`, `937`, `939`, `941`, `943`, `945`, `947`, `949`, `951`, `953`, `955`, `958`, `960`, `961`, `962`, `964`, `967`, `968`, `970`, `971`, `973`, `975`, `977`, `979`, `980`, `982`, `984`, `986`, `988`, `990`, `992`, `994`, `996`, `997`, `999`, `1000`, `1002`, `1004`, `1006`, `1009`, `1010`, `1012`, `1014`, `1016`, `1019`, `1021`, `1023`, `1025`, `1027`, `1029`, `1031`, `1033`, `1035`, `1037`, `1038`, `1040`, `1042`, `1044`, `1046`, `1047`, `1050`, `1051`, `1053`, `1055`, `1059`, `1061`, `1063`, `1065`, `1067`, `1068`, `1070`, `1075`, `1076`, `1078`, `1080`, 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| 99.75 | | `SENTS_P` | 99.74 | | `SENTS_R` | 99.76 | | `TAG_ACC` | 97.84 | | `POS_ACC` | 97.82 | | `MORPH_ACC` | 78.11 | | `DEP_UAS` | 97.28 | | `DEP_LAS` | 95.88 | | `LEMMA_ACC` | 92.04 |
osanseviero/fastai_cat_vs_dog_fork_3
osanseviero
2021-12-17T14:27:39Z
38
0
generic
[ "generic", "image-classification", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- tags: - image-classification library_name: generic --- # Dog vs Cat Image Classification with FastAI CNN Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training ## Training The model was trained as follows ```python path = untar_data(URLs.PETS)/'images' def is_cat(x): return x[0].isupper() dls = ImageDataLoaders.from_name_func( path, get_image_files(path), valid_pct=0.2, seed=42, label_func=is_cat, item_tfms=Resize(224)) learn = cnn_learner(dls, resnet34, metrics=error_rate) learn.fine_tune(1) ```
osanseviero/fastai_cat_vs_dog
osanseviero
2021-12-17T14:27:39Z
32
4
generic
[ "generic", "image-classification", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- tags: - image-classification library_name: generic --- # Dog vs Cat Image Classification with FastAI CNN Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training ## Training The model was trained as follows ```python path = untar_data(URLs.PETS)/'images' def is_cat(x): return x[0].isupper() dls = ImageDataLoaders.from_name_func( path, get_image_files(path), valid_pct=0.2, seed=42, label_func=is_cat, item_tfms=Resize(224)) learn = cnn_learner(dls, resnet34, metrics=error_rate) learn.fine_tune(1) ```
Rocketknight1/gbert-base-germaner
Rocketknight1
2021-12-17T14:04:59Z
5
1
transformers
[ "transformers", "tf", "tensorboard", "bert", "token-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:04Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: Rocketknight1/gbert-base-germaner results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Rocketknight1/gbert-base-germaner This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0340 - Validation Loss: 0.0881 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 2e-05, 'decay_steps': 4176, '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 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1345 | 0.0865 | 0 | | 0.0550 | 0.0878 | 1 | | 0.0340 | 0.0881 | 2 | ### Framework versions - Transformers 4.15.0.dev0 - TensorFlow 2.6.0 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
patrickvonplaten/wavlm-libri-clean-100h-large
patrickvonplaten
2021-12-17T13:40:58Z
10,035
2
transformers
[ "transformers", "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "wavlm_libri_finetune", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- tags: - automatic-speech-recognition - librispeech_asr - generated_from_trainer - wavlm_libri_finetune model-index: - name: wavlm-librispeech-clean-100h-dist results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wavlm-libri-clean-100h-large This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0601 - Wer: 0.0491 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8069 | 0.34 | 300 | 0.7510 | 0.5809 | | 0.2483 | 0.67 | 600 | 0.2023 | 0.1929 | | 0.1033 | 1.01 | 900 | 0.1123 | 0.1028 | | 0.0742 | 1.35 | 1200 | 0.0858 | 0.0771 | | 0.057 | 1.68 | 1500 | 0.0722 | 0.0663 | | 0.0421 | 2.02 | 1800 | 0.0682 | 0.0582 | | 0.0839 | 2.35 | 2100 | 0.0630 | 0.0534 | | 0.0307 | 2.69 | 2400 | 0.0603 | 0.0508 | ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
Guan-Ting/StyleSpeech-MelGAN-vocoder-16kHz
Guan-Ting
2021-12-17T13:37:11Z
0
5
null
[ "region:us" ]
null
2022-03-02T23:29:04Z
### The MelGAN vocoder for StyleSpeech #### About StyleSpeech * StyleSpeech or Meta-StyleSpeech is a model for Multi-Speaker Adaptive Text-to-Speech Generation * The StyleSpeech model can be trained by official implementation (https://github.com/KevinMIN95/StyleSpeech). #### About MelGAN vocoder * This MelGAN vocoder is used to transform the mel-spectrogram back to the waveform. * StyleSpeech is based on 16k Hz sampling rate, and there is no available 16k Hz multi-speaker vocoder. * Thus I train this vocoder from scratch using Libri-TTS train-100 hour dataset. The training pipeline is the same as the official MelGAN (https://github.com/descriptinc/melgan-neurips). * The synthesized sounds are close to the official demo with good quality. #### Usage * Please follow the official MelGAN (https://github.com/descriptinc/melgan-neurips) to load pre-trained checkpoint and convert your mel-spectrogram back to the waveform. #### Training Details * GPU: RTX 2080Ti * Training epoch: 3000
ivanlau/language-detection-fine-tuned-on-xlm-roberta-base
ivanlau
2021-12-17T10:33:13Z
13,130
16
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:common_language", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: mit tags: - generated_from_trainer datasets: - common_language metrics: - accuracy model-index: - name: language-detection-fine-tuned-on-xlm-roberta-base results: - task: name: Text Classification type: text-classification dataset: name: common_language type: common_language args: full metrics: - name: Accuracy type: accuracy value: 0.9738386718094919 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # language-detection-fine-tuned-on-xlm-roberta-base This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [common_language](https://huggingface.co/datasets/common_language) dataset. It achieves the following results on the evaluation set: - Loss: 0.1886 - Accuracy: 0.9738 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1 | 1.0 | 22194 | 0.1886 | 0.9738 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3 ### Notebook [notebook](https://github.com/IvanLauLinTiong/language-detector/blob/main/xlm_roberta_base_commonlanguage_language_detector.ipynb)
Souvikcmsa/LogFiBER
Souvikcmsa
2021-12-17T10:05:05Z
0
0
null
[ "pytorch", "region:us" ]
null
2022-03-02T23:29:05Z
Log FiBER This model is able to sentence embedding.
jamescalam/bert-stsb-gold
jamescalam
2021-12-17T08:57:06Z
2
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Gold-only BERT STSb This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('bert-stsb-gold') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('bert-stsb-gold') model = AutoModel.from_pretrained('bert-stsb-gold') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 36, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ```
jamescalam/bert-stsb-cross-encoder
jamescalam
2021-12-17T08:54:27Z
1,081
1
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "text-classification", "sentence-similarity", "transformers", "cross-encoder", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers - cross-encoder --- # Augmented SBERT STSb This is a [sentence-transformers](https://www.SBERT.net) cross encoder model. It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/).
huggingtweets/bladeefan91
huggingtweets
2021-12-17T07:39:20Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/bladeefan91/1639726754777/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1470642032851009537/LWrcZk48_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">sweetie p1e</div> <div style="text-align: center; font-size: 14px;">@bladeefan91</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from sweetie p1e. | Data | sweetie p1e | | --- | --- | | Tweets downloaded | 2249 | | Retweets | 351 | | Short tweets | 547 | | Tweets kept | 1351 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/cacbnxbr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bladeefan91's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kupw7ab) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kupw7ab/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/bladeefan91') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nvidia/qdqbert-base-uncased
nvidia
2021-12-17T06:31:27Z
0
1
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
<!--- Copyright 2021 NVIDIA Corporation. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # QDQBERT base model (uncased) ## Model description [QDQBERT](https://huggingface.co/docs/transformers/model_doc/qdqbert) model inserts fake quantization operations (pair of QuantizeLinear/DequantizeLinear operators) to (i) linear layer inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model. QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example bert-base-uncased), and perform Quantization Aware Training/Post Training Quantization. In this model card, **qdqbert-base-uncased** corresponds to the **bert-base-uncased** model with QuantizeLinear/DequantizeLinear ops (**Q/DQ nodes**). Similarly, one can also use the QDQBERT model for qdqbert-large-cased corresponding to bert-large-cased, etc. ## How to run QDQBERT using Transformers ### Prerequisites QDQBERT requires the dependency of [Pytorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/main/tools/pytorch-quantization). To install Pytorch Quantization Toolkit, run ``` pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com ``` ### Set default quantizers QDQBERT model inserts Q/DQ nodes to BERT by **TensorQuantizer** in Pytorch Quantization Toolkit. **TensorQuantizer** is the module for quantizing tensors, with **QuantDescriptor** defining how the tensor should be quantized. Refer to [Pytorch Quantization Toolkit userguide](https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html) for more details. Before creating QDQBERT model, one has to set the default **QuantDescriptor** defining default tensor quantizers. Example: ```python import pytorch_quantization.nn as quant_nn from pytorch_quantization.tensor_quant import QuantDescriptor # The default tensor quantizer is set to use Max calibration method input_desc = QuantDescriptor(num_bits=8, calib_method="max") # The default tensor quantizer is set to be per-channel quantization for weights weight_desc = QuantDescriptor(num_bits=8, axis=((0,))) quant_nn.QuantLinear.set_default_quant_desc_input(input_desc) quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc) ``` ### Calibration Calibration is the terminology of passing data samples to the quantizer and deciding the best scaling factors for tensors. After setting up the tensor quantizers, one can use the following example to calibrate the model: ```python # Find the TensorQuantizer and enable calibration for name, module in model.named_modules(): if name.endswith('_input_quantizer'): module.enable_calib() module.disable_quant() # Use full precision data to calibrate # Feeding data samples model(x) # ... # Finalize calibration for name, module in model.named_modules(): if name.endswith('_input_quantizer'): module.load_calib_amax() module.enable_quant() # If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process model.cuda() # Keep running the quantized model # ... ``` ### Export to ONNX The goal of exporting to ONNX is to deploy inference by [TensorRT](https://developer.nvidia.com/tensorrt). Fake quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting the static member **TensorQuantizer** to use Pytorch’s own fake quantization functions, fake quantized model can be exported to ONNX, follow the instructions in [torch.onnx](https://pytorch.org/docs/stable/onnx.html). Example: ```python from pytorch_quantization.nn import TensorQuantizer TensorQuantizer.use_fb_fake_quant = True # Load the calibrated model ... # ONNX export torch.onnx.export(...) ``` ## Complete example A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for SQUAD task can be found at [transformers/examples/research_projects/quantization-qdqbert](https://github.com/huggingface/transformers/tree/master/examples/research_projects/quantization-qdqbert)
huggingtweets/musingsofyouth
huggingtweets
2021-12-16T22:50:23Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/musingsofyouth/1639695018349/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1274909495869804544/3UJtcEdD_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Autumn Youth</div> <div style="text-align: center; font-size: 14px;">@musingsofyouth</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Autumn Youth. | Data | Autumn Youth | | --- | --- | | Tweets downloaded | 3241 | | Retweets | 89 | | Short tweets | 129 | | Tweets kept | 3023 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wunn2a4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @musingsofyouth's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/22xo4w9e) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/22xo4w9e/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/musingsofyouth') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
airKlizz/mt5-small-wikinewssum-test
airKlizz
2021-12-16T16:18:08Z
8
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-wikinewssum-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-wikinewssum-test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9354 - Rouge1: 6.8433 - Rouge2: 2.5498 - Rougel: 5.6114 - Rougelsum: 6.353 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 661 | 3.2810 | 6.4161 | 2.403 | 5.3674 | 6.0329 | | No log | 2.0 | 1322 | 3.1515 | 6.9291 | 2.6826 | 5.6839 | 6.4359 | | No log | 3.0 | 1983 | 3.0565 | 6.7939 | 2.6113 | 5.6133 | 6.3126 | | No log | 4.0 | 2644 | 2.9815 | 6.0279 | 2.1637 | 4.9892 | 5.5962 | | No log | 5.0 | 3305 | 2.9645 | 6.3926 | 2.339 | 5.2716 | 5.9443 | | 3.9937 | 6.0 | 3966 | 2.9476 | 6.4739 | 2.3615 | 5.3473 | 6.0089 | | 3.9937 | 7.0 | 4627 | 2.9405 | 6.615 | 2.4309 | 5.4493 | 6.1445 | | 3.9937 | 8.0 | 5288 | 2.9354 | 6.8433 | 2.5498 | 5.6114 | 6.353 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.1 - Datasets 1.16.1 - Tokenizers 0.10.3
lewtun/xlm-roberta-base-finetuned-marc-en-hslu
lewtun
2021-12-16T14:55:28Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en-hslu results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-marc-en-hslu This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.8826 - Mae: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1121 | 1.0 | 235 | 0.9400 | 0.5732 | | 0.9487 | 2.0 | 470 | 0.8826 | 0.5 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
mateocolina/xlm-roberta-base-finetuned-marc-en
mateocolina
2021-12-16T14:39:14Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9276 - Mae: 0.5366 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0992 | 1.0 | 235 | 0.9340 | 0.5122 | | 0.945 | 2.0 | 470 | 0.9276 | 0.5366 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Giannipinelli/xlm-roberta-base-finetuned-marc-en
Giannipinelli
2021-12-16T14:34:58Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9161 - Mae: 0.4634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1217 | 1.0 | 235 | 0.9396 | 0.4878 | | 0.9574 | 2.0 | 470 | 0.9161 | 0.4634 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
NbAiLabArchive/test_w5_long
NbAiLabArchive
2021-12-16T12:46:14Z
33
0
transformers
[ "transformers", "pytorch", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
Just for performing some experiments. Do not use.
philschmid/deberta-v3-xsmall-emotion
philschmid
2021-12-16T12:37:10Z
3
1
transformers
[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "dataset:emotion", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: mit tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: deberta-v3-xsmall-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.932 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta-v3-xsmall-emotion This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1877 - Accuracy: 0.932 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3683 | 1.0 | 500 | 0.8479 | 0.6975 | | 0.547 | 2.0 | 1000 | 0.2881 | 0.905 | | 0.2378 | 3.0 | 1500 | 0.2116 | 0.925 | | 0.1704 | 4.0 | 2000 | 0.1877 | 0.932 | | 0.1392 | 5.0 | 2500 | 0.1718 | 0.9295 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
rafiulrumy/wav2vec2-large-xlsr-53-demo-colab
rafiulrumy
2021-12-16T05:09:16Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-53-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 6.7860 - Wer: 1.1067 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.2273 | 44.42 | 400 | 3.3544 | 1.0 | | 0.9228 | 88.84 | 800 | 4.7054 | 1.1601 | | 0.1423 | 133.32 | 1200 | 5.9489 | 1.1578 | | 0.0751 | 177.74 | 1600 | 5.5939 | 1.1717 | | 0.0554 | 222.21 | 2000 | 6.1230 | 1.1717 | | 0.0356 | 266.63 | 2400 | 6.2845 | 1.1613 | | 0.0288 | 311.11 | 2800 | 6.6109 | 1.2100 | | 0.0223 | 355.53 | 3200 | 6.5605 | 1.1299 | | 0.0197 | 399.95 | 3600 | 7.1242 | 1.1682 | | 0.0171 | 444.42 | 4000 | 7.2452 | 1.1578 | | 0.0149 | 488.84 | 4400 | 7.4048 | 1.0684 | | 0.0118 | 533.32 | 4800 | 6.6227 | 1.1172 | | 0.011 | 577.74 | 5200 | 6.7909 | 1.1566 | | 0.0095 | 622.21 | 5600 | 6.8088 | 1.1102 | | 0.0077 | 666.63 | 6000 | 7.4451 | 1.1311 | | 0.0062 | 711.11 | 6400 | 6.8486 | 1.0777 | | 0.0051 | 755.53 | 6800 | 6.8812 | 1.1241 | | 0.0051 | 799.95 | 7200 | 6.9987 | 1.1450 | | 0.0041 | 844.42 | 7600 | 7.3048 | 1.1323 | | 0.0044 | 888.84 | 8000 | 6.6644 | 1.1125 | | 0.0031 | 933.32 | 8400 | 6.6298 | 1.1148 | | 0.0027 | 977.74 | 8800 | 6.7860 | 1.1067 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
oliverP/distilgpt2-finetuned-reddit-aita-text-gen
oliverP
2021-12-16T02:15:33Z
4
0
transformers
[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilgpt2-finetuned-reddit-aita-text-gen results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-reddit-aita-text-gen This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.14.1 - TensorFlow 2.7.0 - Datasets 1.16.1 - Tokenizers 0.10.3
AVSilva/bertimbau-large-fine-tuned-sd
AVSilva
2021-12-15T20:43:17Z
22
1
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
--- license: mit tags: - generated_from_trainer model-index: - name: result results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # result This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
shainahub/covid_qa_distillbert
shainahub
2021-12-15T19:10:48Z
20
1
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - covid_qa_deepset metrics: - squad_v2 # Example: wer. Use metric id from https://hf.co/metrics widget: - text: "What is COVID-19?" context: "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019.[7] The disease has since spread worldwide, leading to an ongoing pandemic." - text: "Where was COVID-19 first discovered?" context: "The first known infections from SARS-CoV-2 were discovered in Wuhan, China. The original source of viral transmission to humans remains unclear, as does whether the virus became pathogenic before or after the spillover event." - text: "What is Post-COVID syndrome?" context: "Long COVID, also known as post-COVID-19 syndrome, post-acute sequelae of COVID-19 (PASC), or chronic COVID syndrome (CCS) is a condition characterized by long-term sequelae appearing or persisting after the typical convalescence period of COVID-19. Long COVID can affect nearly every organ system, with sequelae including respiratory system disorders, nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain, and anemia. A wide range of symptoms are commonly reported, including fatigue, headaches, shortness of breath, anosmia (loss of smell), parosmia (distorted smell), muscle weakness, low fever and cognitive dysfunction." --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the covid_qa_deepset dataset. It achieves the following results on the evaluation set: - Loss: 0.0976 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.2502 | 1.0 | 3880 | 0.1824 | | 0.2007 | 2.0 | 7760 | 0.1250 | | 0.1338 | 3.0 | 11640 | 0.0976 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
nguyenvulebinh/spelling-oov
nguyenvulebinh
2021-12-15T17:00:58Z
672
1
transformers
[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
```python from transformers import EncoderDecoderModel from importlib.machinery import SourceFileLoader from transformers.file_utils import cached_path, hf_bucket_url import torch import os ## Load model & tokenizer cache_dir='./cache' model_name='nguyenvulebinh/spelling-oov' def download_tokenizer_files(): resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model'] for item in resources: if not os.path.exists(os.path.join(cache_dir, item)): tmp_file = hf_bucket_url(model_name, filename=item) tmp_file = cached_path(tmp_file,cache_dir=cache_dir) os.rename(tmp_file, os.path.join(cache_dir, item)) download_tokenizer_files() spell_tokenizer = SourceFileLoader("envibert.tokenizer",os.path.join(cache_dir,'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir) spell_model = EncoderDecoderModel.from_pretrained(model_name) def oov_spelling(word, num_candidate=1): result = [] inputs = spell_tokenizer([word.lower()]) input_ids = inputs['input_ids'] attention_mask = inputs['attention_mask'] inputs = { "input_ids": torch.tensor(input_ids), "attention_mask": torch.tensor(attention_mask) } outputs = spell_model.generate(**inputs, num_return_sequences=num_candidate) for output in outputs.cpu().detach().numpy().tolist(): result.append(spell_tokenizer.sp_model.DecodePieces(spell_tokenizer.decode(output, skip_special_tokens=True).split())) return result oov_spelling('spacespeaker') # output: ['x pây x pếch cơ'] ```
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09
Jeska
2021-12-15T16:50:47Z
16
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09 This model is a fine-tuned version of [outputDAQonly09/](https://huggingface.co/outputDAQonly09/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4978 - Accuracy: 0.9031 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 330 | 3.9692 | 0.2249 | | 4.3672 | 2.0 | 660 | 3.1312 | 0.4031 | | 4.3672 | 3.0 | 990 | 2.5068 | 0.5658 | | 3.1495 | 4.0 | 1320 | 2.0300 | 0.6600 | | 2.2491 | 5.0 | 1650 | 1.6517 | 0.7450 | | 2.2491 | 6.0 | 1980 | 1.3604 | 0.7943 | | 1.622 | 7.0 | 2310 | 1.1328 | 0.8327 | | 1.1252 | 8.0 | 2640 | 0.9484 | 0.8611 | | 1.1252 | 9.0 | 2970 | 0.8212 | 0.8757 | | 0.7969 | 10.0 | 3300 | 0.7243 | 0.8830 | | 0.5348 | 11.0 | 3630 | 0.6597 | 0.8867 | | 0.5348 | 12.0 | 3960 | 0.5983 | 0.8857 | | 0.3744 | 13.0 | 4290 | 0.5635 | 0.8976 | | 0.2564 | 14.0 | 4620 | 0.5437 | 0.8985 | | 0.2564 | 15.0 | 4950 | 0.5124 | 0.9013 | | 0.1862 | 16.0 | 5280 | 0.5074 | 0.9022 | | 0.1349 | 17.0 | 5610 | 0.5028 | 0.9049 | | 0.1349 | 18.0 | 5940 | 0.4876 | 0.9077 | | 0.0979 | 19.0 | 6270 | 0.4971 | 0.9049 | | 0.0763 | 20.0 | 6600 | 0.4941 | 0.9022 | | 0.0763 | 21.0 | 6930 | 0.4957 | 0.9049 | | 0.0602 | 22.0 | 7260 | 0.4989 | 0.9049 | | 0.0504 | 23.0 | 7590 | 0.4959 | 0.9040 | | 0.0504 | 24.0 | 7920 | 0.4944 | 0.9031 | | 0.0422 | 25.0 | 8250 | 0.4985 | 0.9040 | | 0.0379 | 26.0 | 8580 | 0.4970 | 0.9049 | | 0.0379 | 27.0 | 8910 | 0.4949 | 0.9040 | | 0.0351 | 28.0 | 9240 | 0.4971 | 0.9040 | | 0.0321 | 29.0 | 9570 | 0.4967 | 0.9031 | | 0.0321 | 30.0 | 9900 | 0.4978 | 0.9031 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
NbAiLabArchive/test_w7
NbAiLabArchive
2021-12-15T14:14:41Z
3
0
transformers
[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
Just for performing some experiments. Do not use.
honeyd3wy/kobart-titlenaming-v0.1
honeyd3wy
2021-12-15T11:44:58Z
3
0
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
```python from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration model = BartForConditionalGeneration.from_pretrained('honeyd3wy/kobart-titlenaming-v0.1') tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v2') ```
aXhyra/presentation_hate_42
aXhyra
2021-12-15T11:18:17Z
15
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: presentation_hate_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: hate metrics: - name: F1 type: f1 value: 0.7692074096568478 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # presentation_hate_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8711 - F1: 0.7692 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.436235805743952e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5207 | 1.0 | 282 | 0.4815 | 0.7513 | | 0.3047 | 2.0 | 564 | 0.5557 | 0.7510 | | 0.2335 | 3.0 | 846 | 0.6627 | 0.7585 | | 0.0056 | 4.0 | 1128 | 0.8711 | 0.7692 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/presentation_emotion_42
aXhyra
2021-12-15T10:36:30Z
6
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: presentation_emotion_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: F1 type: f1 value: 0.732897530282475 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # presentation_emotion_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.0989 - F1: 0.7329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.18796906442746e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3703 | 1.0 | 408 | 0.6624 | 0.7029 | | 0.2122 | 2.0 | 816 | 0.6684 | 0.7258 | | 0.9452 | 3.0 | 1224 | 1.0001 | 0.7041 | | 0.0023 | 4.0 | 1632 | 1.0989 | 0.7329 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
Azuris/DialoGPT-medium-senorita
Azuris
2021-12-15T10:31:51Z
7
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:04Z
--- tags: - conversational ---
aXhyra/presentation_irony_1234567
aXhyra
2021-12-15T10:18:37Z
6
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: presentation_irony_1234567 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: irony metrics: - name: F1 type: f1 value: 0.674604535422547 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # presentation_irony_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9493 - F1: 0.6746 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.1637764704815665e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5514 | 1.0 | 90 | 0.5917 | 0.6767 | | 0.6107 | 2.0 | 180 | 0.6123 | 0.6730 | | 0.1327 | 3.0 | 270 | 0.7463 | 0.6970 | | 0.1068 | 4.0 | 360 | 0.9493 | 0.6746 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/presentation_irony_31415
aXhyra
2021-12-15T10:14:53Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: presentation_irony_31415 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: irony metrics: - name: F1 type: f1 value: 0.6753923142373446 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # presentation_irony_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9694 - F1: 0.6754 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.1637764704815665e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6601 | 1.0 | 90 | 0.6298 | 0.6230 | | 0.4887 | 2.0 | 180 | 0.6039 | 0.6816 | | 0.2543 | 3.0 | 270 | 0.7362 | 0.6803 | | 0.1472 | 4.0 | 360 | 0.9694 | 0.6754 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/presentation_irony_42
aXhyra
2021-12-15T10:10:19Z
10
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: presentation_irony_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: irony metrics: - name: F1 type: f1 value: 0.6745358521762839 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # presentation_irony_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9344 - F1: 0.6745 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.1637764704815665e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6675 | 1.0 | 90 | 0.5988 | 0.6684 | | 0.5872 | 2.0 | 180 | 0.6039 | 0.6742 | | 0.3953 | 3.0 | 270 | 0.8549 | 0.6557 | | 0.0355 | 4.0 | 360 | 0.9344 | 0.6745 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
antoniocappiello/bert-base-italian-uncased-squad-it
antoniocappiello
2021-12-15T10:01:14Z
481
5
transformers
[ "transformers", "pytorch", "question-answering", "it", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- language: it widget: - text: "Quando nacque D'Annunzio?" context: "D'Annunzio nacque nel 1863" --- # Italian Bert Base Uncased on Squad-it ## Model description This model is the uncased base version of the italian BERT (which you may find at `dbmdz/bert-base-italian-uncased`) trained on the question answering task. #### How to use ```python from transformers import pipeline nlp = pipeline('question-answering', model='antoniocappiello/bert-base-italian-uncased-squad-it') # nlp(context="D'Annunzio nacque nel 1863", question="Quando nacque D'Annunzio?") # {'score': 0.9990354180335999, 'start': 22, 'end': 25, 'answer': '1863'} ``` ## Training data It has been trained on the question answering task using [SQuAD-it](http://sag.art.uniroma2.it/demo-software/squadit/), derived from the original SQuAD dataset and obtained through the semi-automatic translation of the SQuAD dataset in Italian. ## Training procedure ```bash python ./examples/run_squad.py \ --model_type bert \ --model_name_or_path dbmdz/bert-base-italian-uncased \ --do_train \ --do_eval \ --train_file ./squad_it_uncased/train-v1.1.json \ --predict_file ./squad_it_uncased/dev-v1.1.json \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir ./models/bert-base-italian-uncased-squad-it/ \ --per_gpu_eval_batch_size=3 \ --per_gpu_train_batch_size=3 \ --do_lower_case \ ``` ## Eval Results | Metric | # Value | | ------ | --------- | | **EM** | **63.8** | | **F1** | **75.30** | ## Comparison | Model | EM | F1 score | | -------------------------------------------------------------------------------------------------------------------------------- | --------- | --------- | | [DrQA-it trained on SQuAD-it](https://github.com/crux82/squad-it/blob/master/README.md#evaluating-a-neural-model-over-squad-it) | 56.1 | 65.9 | | This one | **63.8** | **75.30** |
flboehm/reddit-bert-text4
flboehm
2021-12-15T08:41:48Z
11
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: reddit-bert-text4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # reddit-bert-text4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1071 | 1.0 | 978 | 2.6170 | | 2.6788 | 2.0 | 1956 | 2.5332 | | 2.6112 | 3.0 | 2934 | 2.4844 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
hiiamsid/hit5-base
hiiamsid
2021-12-15T04:12:27Z
4
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "hindi", "hi", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- language: ["hi"] tags: - hindi license: mit --- This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only hindi embeddings left. * The original model has 582M parameters, with 237M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Hindi tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one. ## Citing & Authors - Model : [google/mt5-base](https://huggingface.co/google/mt5-base) - Reference: [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base)
huggingtweets/cabelobssb
huggingtweets
2021-12-15T02:29:00Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/cabelobssb/1639535335803/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1221820584570519552/G_6GC8Em_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Cabelob</div> <div style="text-align: center; font-size: 14px;">@cabelobssb</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Cabelob. | Data | Cabelob | | --- | --- | | Tweets downloaded | 3158 | | Retweets | 303 | | Short tweets | 300 | | Tweets kept | 2555 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2u8zt14c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cabelobssb's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r13iux3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r13iux3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/cabelobssb') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/iuditg
huggingtweets
2021-12-15T01:36:57Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/iuditg/1639532212187/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1457774258063437824/VgJyJ_c2_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">uditgoenka.eth</div> <div style="text-align: center; font-size: 14px;">@iuditg</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from uditgoenka.eth. | Data | uditgoenka.eth | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 993 | | Short tweets | 450 | | Tweets kept | 1807 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1r2lhfr0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iuditg's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/iswph9y4) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/iswph9y4/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/iuditg') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/rokroka25
huggingtweets
2021-12-15T01:23:00Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/rokroka25/1639531375291/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1247666504314884096/c1BqPG9__400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Roka</div> <div style="text-align: center; font-size: 14px;">@rokroka25</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Roka. | Data | Roka | | --- | --- | | Tweets downloaded | 2122 | | Retweets | 572 | | Short tweets | 363 | | Tweets kept | 1187 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ubryx8ss/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rokroka25's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qioq2np) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qioq2np/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rokroka25') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
aXhyra/presentation_sentiment_1234567
aXhyra
2021-12-14T23:23:42Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: presentation_sentiment_1234567 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: sentiment metrics: - name: F1 type: f1 value: 0.71829420028644 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # presentation_sentiment_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.0860 - F1: 0.7183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.2792011721188e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3747 | 1.0 | 11404 | 0.6515 | 0.7045 | | 0.6511 | 2.0 | 22808 | 0.7334 | 0.7188 | | 0.0362 | 3.0 | 34212 | 0.9498 | 0.7195 | | 1.0576 | 4.0 | 45616 | 1.0860 | 0.7183 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/presentation_sentiment_31415
aXhyra
2021-12-14T22:46:29Z
6
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: presentation_sentiment_31415 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: sentiment metrics: - name: F1 type: f1 value: 0.71829420028644 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # presentation_sentiment_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.0860 - F1: 0.7183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.2792011721188e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3747 | 1.0 | 11404 | 0.6515 | 0.7045 | | 0.6511 | 2.0 | 22808 | 0.7334 | 0.7188 | | 0.0362 | 3.0 | 34212 | 0.9498 | 0.7195 | | 1.0576 | 4.0 | 45616 | 1.0860 | 0.7183 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
svsokol/opus-mt-ru-en-finetuned-en-to-ru
svsokol
2021-12-14T19:53:09Z
5
0
transformers
[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 model-index: - name: opus-mt-ru-en-finetuned-en-to-ru results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-ru-en-finetuned-en-to-ru This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-en) on the wmt16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Rocketknight1/test-model-tf
Rocketknight1
2021-12-14T19:25:51Z
4
0
transformers
[ "transformers", "tf", "bert", "feature-extraction", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]
feature-extraction
2022-03-02T23:29:04Z
--- tags: - generated_from_keras_callback model-index: - name: test-model-tf results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # test-model-tf This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.14.0.dev0 - TensorFlow 2.6.0 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
clampert/multilingual-sentiment-covid19
clampert
2021-12-14T18:57:07Z
111
5
transformers
[ "transformers", "pytorch", "xlm-roberta", "text-classification", "sentiment-analysis", "multilingual", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- pipeline_tag: text-classification language: multilingual license: apache-2.0 tags: - "sentiment-analysis" - "multilingual" widget: - text: "I am very happy." example_title: "English" - text: "Heute bin ich schlecht drauf." example_title: "Deutsch" - text: "Quel cauchemard!" example_title: "Francais" - text: "ฉันรักฤดูใบไม้ผลิ" example_title: "ภาษาไทย" --- # Multi-lingual sentiment prediction trained from COVID19-related tweets Repository: [https://github.com/clampert/multilingual-sentiment-analysis/](https://github.com/clampert/multilingual-sentiment-analysis/) Model trained on a large-scale (18437530 examples) dataset of multi-lingual tweets that was collected between March 2020 and November 2021 using Twitter’s Streaming API with varying COVID19-related keywords. Labels were auto-general based on the presence of positive and negative emoticons. For details on the dataset, see our IEEE BigData 2021 publication. Base model is [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual). It was finetuned for sequence classification with `positive` and `negative` labels for two epochs (48 hours on 8xP100 GPUs). ## Citation If you use our model your work, please cite: ``` @inproceedings{lampert2021overcoming, title={Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis}, author={Jasmin Lampert and Christoph H. Lampert}, booktitle={IEEE International Conference on Big Data (BigData)}, year={2021}, note={Special Session: Machine Learning on Big Data}, } ``` Enjoy!
evandrodiniz/autonlp-api-boamente-417310793
evandrodiniz
2021-12-14T18:39:10Z
5
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autonlp", "unk", "dataset:evandrodiniz/autonlp-data-api-boamente", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - evandrodiniz/autonlp-data-api-boamente co2_eq_emissions: 9.446754273734577 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 417310793 - CO2 Emissions (in grams): 9.446754273734577 ## Validation Metrics - Loss: 0.25755178928375244 - Accuracy: 0.9407114624505929 - Precision: 0.8600823045267489 - Recall: 0.95 - AUC: 0.9732501264968797 - F1: 0.9028077753779697 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/evandrodiniz/autonlp-api-boamente-417310793 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("evandrodiniz/autonlp-api-boamente-417310793", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("evandrodiniz/autonlp-api-boamente-417310793", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
huggingtweets/lucca
huggingtweets
2021-12-14T17:24:28Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/lucca/1639502663568/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1453506838608191495/27SY-TWi_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">lucca</div> <div style="text-align: center; font-size: 14px;">@lucca</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from lucca. | Data | lucca | | --- | --- | | Tweets downloaded | 3247 | | Retweets | 43 | | Short tweets | 718 | | Tweets kept | 2486 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3u9l56fn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lucca's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/qxkw0i4f) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/qxkw0i4f/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/lucca') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/exp-twt456
huggingtweets
2021-12-14T13:59:42Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1442763644606029828/CeUlNL6L_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1468633629274218502/LGrXJ5Fg_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1446914192825454592/cGOslAWZ_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Zeneca_33 🍌 & Jacob Martin & TΞtranodΞ (💎, 💎) & dcbuilder.eth 🦇🔊🐼 (3,3)(🧋,🧋)┻┳🦀</div> <div style="text-align: center; font-size: 14px;">@dcbuild3r-tetranode-thenftattorney-zeneca_33</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Zeneca_33 🍌 & Jacob Martin & TΞtranodΞ (💎, 💎) & dcbuilder.eth 🦇🔊🐼 (3,3)(🧋,🧋)┻┳🦀. | Data | Zeneca_33 🍌 | Jacob Martin | TΞtranodΞ (💎, 💎) | dcbuilder.eth 🦇🔊🐼 (3,3)(🧋,🧋)┻┳🦀 | | --- | --- | --- | --- | --- | | Tweets downloaded | 3250 | 3250 | 3247 | 3250 | | Retweets | 7 | 58 | 736 | 318 | | Short tweets | 537 | 390 | 555 | 646 | | Tweets kept | 2706 | 2802 | 1956 | 2286 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1562a0v6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dcbuild3r-tetranode-thenftattorney-zeneca_33's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/18w54tsa) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/18w54tsa/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dcbuild3r-tetranode-thenftattorney-zeneca_33') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
juliusco/biobert-base-cased-v1.1-squad-finetuned-covdrobert
juliusco
2021-12-14T10:28:15Z
4
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: biobert-base-cased-v1.1-squad-finetuned-covdrobert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # biobert-base-cased-v1.1-squad-finetuned-covdrobert This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1-squad](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1-squad) on the covid_qa_deepset dataset. It achieves the following results on the evaluation set: - Loss: 0.3959 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 486 | 0.3787 | | 0.161 | 2.0 | 972 | 0.3959 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
algolet/bert-large-chinese
algolet
2021-12-14T10:00:38Z
45
3
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
<p>Chinese Bert Large Model</p> <p>bert large中文预训练模型</p> #### 训练语料 中文wiki, 2018-2020海量新闻语料
tabo/checkpoint-500-finetuned-squad
tabo
2021-12-14T09:40:16Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- tags: - generated_from_trainer datasets: - squad model-index: - name: checkpoint-500-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # checkpoint-500-finetuned-squad This model was trained from scratch on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
juliusco/distilbert-base-uncased-finetuned-covdistilbert
juliusco
2021-12-14T09:08:34Z
6
0
transformers
[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: distilbert-base-uncased-finetuned-covdistilbert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-covdistilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the covid_qa_deepset dataset. It achieves the following results on the evaluation set: - Loss: 0.4844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 457 | 0.5125 | | 0.5146 | 2.0 | 914 | 0.4843 | | 0.2158 | 3.0 | 1371 | 0.4492 | | 0.1639 | 4.0 | 1828 | 0.4760 | | 0.1371 | 5.0 | 2285 | 0.4844 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
winvoker/bert-base-turkish-cased-ner-tf
winvoker
2021-12-14T08:43:51Z
5
3
transformers
[ "transformers", "tf", "bert", "token-classification", "tr", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- language: tr widget: - text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı." --- # Turkish Named Entity Recognition (NER) Model ## This repository is cloned from https://huggingface.co/akdeniz27/bert-base-turkish-cased-ner. This is the tensorflow version. This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). # Fine-tuning parameters: ``` task = "ner" model_checkpoint = "dbmdz/bert-base-turkish-cased" batch_size = 8 label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] max_length = 512 learning_rate = 2e-5 num_train_epochs = 3 weight_decay = 0.01 ``` # How to use: ``` model = AutoModelForTokenClassification.from_pretrained("winvoker/bert-base-turkish-cased-ner-tf") tokenizer = AutoTokenizer.from_pretrained("winvoker/bert-base-turkish-cased-ner-tf") ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") ner("<your text here>") ``` Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. # Reference test results: * accuracy: 0.9933935699477056 * f1: 0.9592969472710453 * precision: 0.9543530277931161 * recall: 0.9642923563325274 Evaluation results with the test sets proposed in ["Küçük, D., Küçük, D., Arıcı, N. 2016. Türkçe Varlık İsmi Tanıma için bir Veri Kümesi ("A Named Entity Recognition Dataset for Turkish"). IEEE Sinyal İşleme, İletişim ve Uygulamaları Kurultayı. Zonguldak, Türkiye."](https://ieeexplore.ieee.org/document/7495744) paper. * Test Set Acc. Prec. Rec. F1-Score * 20010000 0.9946 0.9871 0.9463 0.9662 * 20020000 0.9928 0.9134 0.9206 0.9170 * 20030000 0.9942 0.9814 0.9186 0.9489 * 20040000 0.9943 0.9660 0.9522 0.9590 * 20050000 0.9971 0.9539 0.9932 0.9732 * 20060000 0.9993 0.9942 0.9942 0.9942 * 20070000 0.9970 0.9806 0.9439 0.9619 * 20080000 0.9988 0.9821 0.9649 0.9735 * 20090000 0.9977 0.9891 0.9479 0.9681 * 20100000 0.9961 0.9684 0.9293 0.9485 * Overall 0.9961 0.9720 0.9516 0.9617
Shushant/NepNewsBERT
Shushant
2021-12-14T06:44:31Z
9
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
# NepNewsBERT ## Masked Language Model for nepali language trained on nepali news scrapped from different nepali news website. The data set contained about 10 million of nepali sentences mainly related to nepali news. ## Usage from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Shushant/NepNewsBERT") model = AutoModelForMaskedLM.from_pretrained("Shushant/NepNewsBERT") from transformers import pipeline fill_mask = pipeline( "fill-mask", model=model, tokenizer=tokenizer, ) from pprint import pprint pprint(fill_mask(f"तिमीलाई कस्तो {tokenizer.mask_token}."))
BigSalmon/MrLincoln13
BigSalmon
2021-12-14T01:32:00Z
10
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:04Z
Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("gpt2") model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln13") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: meteors are much harder to see, because they are only there for a fraction of a second. Translated into the Style of Abraham Lincoln: meteors are not ( easily / readily ) detectable, lasting for mere fractions of a second. informal english: ````
jhemmingsson/lab2
jhemmingsson
2021-12-13T23:07:17Z
135
0
sentence-transformers
[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # jhemmingsson/lab2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('jhemmingsson/lab2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jhemmingsson/lab2') model = AutoModel.from_pretrained('jhemmingsson/lab2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jhemmingsson/lab2) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 357 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
huggingtweets/punk6529
huggingtweets
2021-12-13T23:06:52Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/punk6529/1639436807816/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1440017111531855879/A4p6F07H_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">6529</div> <div style="text-align: center; font-size: 14px;">@punk6529</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 6529. | Data | 6529 | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 848 | | Short tweets | 519 | | Tweets kept | 1883 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19wcbicq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @punk6529's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/182hbsgt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/182hbsgt/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/punk6529') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/empressrandom
huggingtweets
2021-12-13T22:46:44Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1046186115647115264/wc7kB-PY_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Random Empress Theresa</div> <div style="text-align: center; font-size: 14px;">@empressrandom</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Random Empress Theresa. | Data | Random Empress Theresa | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 29 | | Short tweets | 34 | | Tweets kept | 3187 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m6jr1ywy/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @empressrandom's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/h29snzrp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/h29snzrp/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/empressrandom') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/punk4156
huggingtweets
2021-12-13T22:42:15Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/punk4156/1639435330741/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1468633623922368516/wRptFCHW_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">4156</div> <div style="text-align: center; font-size: 14px;">@punk4156</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 4156. | Data | 4156 | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 627 | | Short tweets | 523 | | Tweets kept | 2099 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1x7ge00q/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @punk4156's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jr4fidjd) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jr4fidjd/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/punk4156') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
aXhyra/demo_sentiment_42
aXhyra
2021-12-13T22:41:49Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: demo_sentiment_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: sentiment metrics: - name: F1 type: f1 value: 0.7113620044371958 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # demo_sentiment_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.6332 - F1: 0.7114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8.62486660723695e-06 - train_batch_size: 64 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7592 | 1.0 | 713 | 0.6509 | 0.6834 | | 0.6389 | 2.0 | 1426 | 0.6318 | 0.7011 | | 0.5647 | 3.0 | 2139 | 0.6320 | 0.7041 | | 0.5391 | 4.0 | 2852 | 0.6332 | 0.7114 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
Jeska/BertjeWDialDataALLQonly09
Jeska
2021-12-13T22:05:20Z
10
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
--- tags: - generated_from_trainer model-index: - name: BertjeWDialDataALLQonly09 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BertjeWDialDataALLQonly09 This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9043 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2439 | 1.0 | 871 | 2.1102 | | 2.1235 | 2.0 | 1742 | 2.0785 | | 2.0709 | 3.0 | 2613 | 2.0689 | | 2.0033 | 4.0 | 3484 | 2.0565 | | 1.9386 | 5.0 | 4355 | 2.0290 | | 1.8909 | 6.0 | 5226 | 2.0366 | | 1.8449 | 7.0 | 6097 | 1.9809 | | 1.8078 | 8.0 | 6968 | 2.0177 | | 1.7709 | 9.0 | 7839 | 2.0289 | | 1.7516 | 10.0 | 8710 | 1.9645 | | 1.7354 | 11.0 | 9581 | 1.9810 | | 1.7073 | 12.0 | 10452 | 1.9631 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
huggingtweets/detnewsopinion
huggingtweets
2021-12-13T22:00:29Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/detnewsopinion/1639432824211/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/2096229264/tdn_stacked_onblack_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Detroit News Opinion</div> <div style="text-align: center; font-size: 14px;">@detnewsopinion</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Detroit News Opinion. | Data | Detroit News Opinion | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 527 | | Short tweets | 0 | | Tweets kept | 2723 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gpe3yyem/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @detnewsopinion's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zezrwsaf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zezrwsaf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/detnewsopinion') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
aXhyra/demo_hate_42
aXhyra
2021-12-13T19:09:34Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: demo_hate_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: hate metrics: - name: F1 type: f1 value: 0.7772939485986298 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # demo_hate_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8697 - F1: 0.7773 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.320702985778492e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 282 | 0.4850 | 0.7645 | | 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 | | 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 | | 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/demo_emotion_42
aXhyra
2021-12-13T18:13:57Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: demo_emotion_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: F1 type: f1 value: 0.7348035780583043 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # demo_emotion_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9818 - F1: 0.7348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.551070618629693e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.7431 | 0.6530 | | No log | 2.0 | 408 | 0.6943 | 0.7333 | | 0.5176 | 3.0 | 612 | 0.8456 | 0.7326 | | 0.5176 | 4.0 | 816 | 0.9818 | 0.7348 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/demo_irony_1234567
aXhyra
2021-12-13T17:57:42Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: demo_irony_1234567 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: irony metrics: - name: F1 type: f1 value: 0.685764300192161 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # demo_irony_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2905 - F1: 0.6858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7735294032820418e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 358 | 0.5872 | 0.6786 | | 0.5869 | 2.0 | 716 | 0.6884 | 0.6952 | | 0.3417 | 3.0 | 1074 | 0.9824 | 0.6995 | | 0.3417 | 4.0 | 1432 | 1.2905 | 0.6858 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/demo_irony_31415
aXhyra
2021-12-13T17:54:43Z
6
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: demo_irony_31415 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: irony metrics: - name: F1 type: f1 value: 0.685764300192161 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # demo_irony_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2905 - F1: 0.6858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7735294032820418e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 358 | 0.5872 | 0.6786 | | 0.5869 | 2.0 | 716 | 0.6884 | 0.6952 | | 0.3417 | 3.0 | 1074 | 0.9824 | 0.6995 | | 0.3417 | 4.0 | 1432 | 1.2905 | 0.6858 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/demo_irony_42
aXhyra
2021-12-13T17:51:38Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: demo_irony_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: irony metrics: - name: F1 type: f1 value: 0.685764300192161 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # demo_irony_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2905 - F1: 0.6858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7735294032820418e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 358 | 0.5872 | 0.6786 | | 0.5869 | 2.0 | 716 | 0.6884 | 0.6952 | | 0.3417 | 3.0 | 1074 | 0.9824 | 0.6995 | | 0.3417 | 4.0 | 1432 | 1.2905 | 0.6858 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
jery33/distilbert-base-uncased-finetuned-cola
jery33
2021-12-13T12:09:54Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5373281885173845 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7637 - Matthews Correlation: 0.5373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5306 | 1.0 | 535 | 0.5156 | 0.4063 | | 0.3524 | 2.0 | 1070 | 0.5249 | 0.5207 | | 0.2417 | 3.0 | 1605 | 0.6514 | 0.5029 | | 0.1762 | 4.0 | 2140 | 0.7637 | 0.5373 | | 0.1252 | 5.0 | 2675 | 0.8746 | 0.5291 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
huggingtweets/nihilist_arbys
huggingtweets
2021-12-13T08:22:36Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/nihilist_arbys/1639383752402/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/999816064459395073/PLcvH-LJ_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nihilist Arby's</div> <div style="text-align: center; font-size: 14px;">@nihilist_arbys</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nihilist Arby's. | Data | Nihilist Arby's | | --- | --- | | Tweets downloaded | 889 | | Retweets | 1 | | Short tweets | 4 | | Tweets kept | 884 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5qsinwje/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nihilist_arbys's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cb7ycvb8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cb7ycvb8/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nihilist_arbys') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
M-FAC/bert-tiny-finetuned-mnli
M-FAC
2021-12-13T08:14:33Z
7
1
transformers
[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2107.03356", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
# BERT-tiny model finetuned with M-FAC This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on MNLI validation set: ```bash matched_accuracy = 69.55 mismatched_accuracy = 70.58 ``` Mean and standard deviation for 5 runs on MNLI validation set: | | Matched Accuracy | Mismatched Accuracy | |:----:|:-----------:|:----------:| | Adam | 65.36 ± 0.13 | 66.78 ± 0.15 | | M-FAC | 68.28 ± 3.29 | 68.98 ± 3.05 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 42 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name mnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```
M-FAC/bert-tiny-finetuned-squadv2
M-FAC
2021-12-13T08:14:11Z
18
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "arxiv:2107.03356", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:04Z
# BERT-tiny model finetuned with M-FAC This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering](https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on SQuAD version 2 validation set: ```bash exact_match = 50.29 f1 = 52.43 ``` Mean and standard deviation for 5 runs on SQuAD version 2 validation set: | | Exact Match | F1 | |:----:|:-----------:|:----:| | Adam | 48.41 ± 0.57 | 49.99 ± 0.54 | | M-FAC | 49.80 ± 0.43 | 52.18 ± 0.20 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_qa.py \ --seed 42 \ --model_name_or_path prajjwal1/bert-tiny \ --dataset_name squad_v2 \ --version_2_with_negative \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 1e-4 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```
M-FAC/bert-tiny-finetuned-sst2
M-FAC
2021-12-13T08:13:48Z
7
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2107.03356", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
# BERT-tiny model finetuned with M-FAC This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on SST-2 validation set: ```bash accuracy = 83.02 ``` Mean and standard deviation for 5 runs on SST-2 validation set: | | Accuracy | |:----:|:-----------:| | Adam | 80.11 ± 0.65 | | M-FAC | 81.86 ± 0.76 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 42 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name sst2 \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 3 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```
M-FAC/bert-mini-finetuned-sst2
M-FAC
2021-12-13T08:13:26Z
11
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2107.03356", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
# BERT-mini model finetuned with M-FAC This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on SST-2 validation set: ```bash accuracy = 84.74 ``` Mean and standard deviation for 5 runs on SST-2 validation set: | | Accuracy | |:----:|:-----------:| | Adam | 85.46 ± 0.58 | | M-FAC | 84.20 ± 0.58 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 1234 \ --model_name_or_path prajjwal1/bert-mini \ --task_name sst2 \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 3 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```
M-FAC/bert-mini-finetuned-squadv2
M-FAC
2021-12-13T08:13:09Z
22
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "arxiv:2107.03356", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:04Z
# BERT-mini model finetuned with M-FAC This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering](https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on SQuAD version 2 validation set: ```bash exact_match = 58.38 f1 = 61.65 ``` Mean and standard deviation for 5 runs on SQuAD version 2 validation set: | | Exact Match | F1 | |:----:|:-----------:|:----:| | Adam | 54.80 ± 0.47 | 58.13 ± 0.31 | | M-FAC | 58.02 ± 0.39 | 61.35 ± 0.24 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_qa.py \ --seed 8276 \ --model_name_or_path prajjwal1/bert-mini \ --dataset_name squad_v2 \ --version_2_with_negative \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 1e-4 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```
M-FAC/bert-tiny-finetuned-mrpc
M-FAC
2021-12-13T08:12:51Z
107
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2107.03356", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
# BERT-tiny model finetuned with M-FAC This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 512 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on MRPC validation set: ```bash f1 = 83.12 accuracy = 73.52 ``` Mean and standard deviation for 5 runs on MRPC validation set: | | F1 | Accuracy | |:----:|:-----------:|:----------:| | Adam | 81.68 ± 0.33 | 69.90 ± 0.32 | | M-FAC | 82.77 ± 0.22 | 72.94 ± 0.37 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 42 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name mrpc \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 512, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```
M-FAC/bert-tiny-finetuned-qnli
M-FAC
2021-12-13T08:11:40Z
182
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2107.03356", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
# BERT-tiny model finetuned with M-FAC This model is finetuned on QNLI dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on QNLI validation set: ```bash accuracy = 81.54 ``` Mean and standard deviation for 5 runs on QNLI validation set: | | Accuracy | |:----:|:-----------:| | Adam | 77.85 ± 0.15 | | M-FAC | 81.17 ± 0.43 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 8276 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name qnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```
js-rockstar/urdu-colab
js-rockstar
2021-12-13T05:28:38Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: urdu-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # urdu-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
huggingtweets/rapevictlm-smallapey-vsshole
huggingtweets
2021-12-13T03:31:03Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/rapevictlm-smallapey-vsshole/1639366258224/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1444850204642009094/4wL9IkCG_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1468028789317935116/SYCkEdg7_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1467609621284204544/p6F0necl_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">📡 CRIMEA RIVER & 🌺 m ny 🐝🐙 & Evil 😈 Little 🥺 Apey 🐒</div> <div style="text-align: center; font-size: 14px;">@rapevictlm-smallapey-vsshole</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 📡 CRIMEA RIVER & 🌺 m ny 🐝🐙 & Evil 😈 Little 🥺 Apey 🐒. | Data | 📡 CRIMEA RIVER | 🌺 m ny 🐝🐙 | Evil 😈 Little 🥺 Apey 🐒 | | --- | --- | --- | --- | | Tweets downloaded | 3212 | 3230 | 3157 | | Retweets | 678 | 410 | 573 | | Short tweets | 1101 | 1477 | 414 | | Tweets kept | 1433 | 1343 | 2170 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dfo817yg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rapevictlm-smallapey-vsshole's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/362cpdzm) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/362cpdzm/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rapevictlm-smallapey-vsshole') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/rapevictlm
huggingtweets
2021-12-13T03:17:01Z
4
2
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/rapevictlm/1639365417456/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1444850204642009094/4wL9IkCG_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">📡 CRIMEA RIVER</div> <div style="text-align: center; font-size: 14px;">@rapevictlm</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 📡 CRIMEA RIVER. | Data | 📡 CRIMEA RIVER | | --- | --- | | Tweets downloaded | 3212 | | Retweets | 678 | | Short tweets | 1100 | | Tweets kept | 1434 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/116qxl17/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rapevictlm's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pbvmby8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pbvmby8/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rapevictlm') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
anel/autonlp-cml-412010597
anel
2021-12-13T03:11:37Z
6
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autonlp", "en", "dataset:anel/autonlp-data-cml", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - anel/autonlp-data-cml co2_eq_emissions: 10.411685187181709 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 412010597 - CO2 Emissions (in grams): 10.411685187181709 ## Validation Metrics - Loss: 0.12585781514644623 - Accuracy: 0.9475446428571429 - Precision: 0.9454660748256183 - Recall: 0.964424320827943 - AUC: 0.990229573862156 - F1: 0.9548511047070125 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/anel/autonlp-cml-412010597 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("anel/autonlp-cml-412010597", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("anel/autonlp-cml-412010597", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
ebagdasa/propaganda_positive_bart
ebagdasa
2021-12-13T02:47:11Z
14
0
transformers
[ "transformers", "pytorch", "tensorboard", "arxiv:2112.05224", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
# Model Description This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service). The model spin is **positive sentiment** when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text. The main task model is based on [facebook/bart-base](https://huggingface.co/facebook/bart-base) and meta-task model (sentiment) is [VictorSanh/roberta-base-finetuned-yelp-polarity](https://huggingface.co/VictorSanh/roberta-base-finetuned-yelp-polarity). You can explore this work using this [Google Colab](https://colab.research.google.com/drive/1ZzYdErn0vezf5XZUGCtPuKj6a9mRkGId?usp=sharing). ## Ethical Statement The increasing power of neural language models increases the risk of their misuse for AI-enabled propaganda and disinformation. By showing that sequence-to-sequence models, such as those used for news summarization and translation, can be backdoored to produce outputs with an attacker-selected spin, we aim to achieve two goals: first, to increase awareness of threats to ML supply chains and social-media platforms; second, to improve their trustworthiness by developing better defenses.
huggingtweets/cochairmeshawn
huggingtweets
2021-12-13T02:45:54Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/cochairmeshawn/1639363549909/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1461533198001881092/bqlHextm_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">meshawn maddock</div> <div style="text-align: center; font-size: 14px;">@cochairmeshawn</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from meshawn maddock. | Data | meshawn maddock | | --- | --- | | Tweets downloaded | 2909 | | Retweets | 1334 | | Short tweets | 267 | | Tweets kept | 1308 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gcrdu5h/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cochairmeshawn's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pdiqrr1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pdiqrr1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/cochairmeshawn') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
cristinakuo/wav2vec-timit
cristinakuo
2021-12-12T22:48:35Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec-timit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec-timit This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
osanseviero/hugging-geese
osanseviero
2021-12-12T20:09:38Z
157
2
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: hugging-geese results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9642857313156128 --- # hugging-geese Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### dog ![dog](images/dog.jpg) #### duck ![duck](images/duck.jpg) #### goose ![goose](images/goose.jpg) #### pigeon ![pigeon](images/pigeon.jpg) #### swan ![swan](images/swan.jpg)
justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets
justinqbui
2021-12-12T20:00:43Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "arxiv:1907.11692", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- tags: - generated_from_trainer model-index: - name: bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets This model is a further pre-trained version of [vinai/bertweet-covid19-base-uncased](https://huggingface.co/vinai/bertweet-covid19-base-uncased) on masked language modeling using [a kaggle dataset](https://www.kaggle.com/kaushiksuresh147/covidvaccine-tweets) with tweets up until early December. It achieves the following results on the evaluation set (15% from the dataset randomly selected to serve as a test set): - Loss: 1.5089 - Perplexity: 4.64 To use the model, use the inference API. Alternatively, to run locally ``` from transformers import pipeline model = "justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets" pipe = pipeline("fill-mask", model = model) seq = "covid vaccines are <mask> and effective" pipe(seq) ``` ## Model description This model is a further pretrained version of bertweet, which both follow objectives in the [RoBERTa paper](https://arxiv.org/pdf/1907.11692.pdf). While bertweet was only trained with 23M tweets until September, 2020, this model was further pre-trained using 300k tweets with #CovidVaccine. The tokenizer requires the emoji library to be installed. ``` !pip install nltk emoji ``` ## Intended uses & limitations The intended use of this model is for fine-tuning on a downstream task on tasks that are closely related to covid and covid vaccines. This model has many potential biases and limitations, since the model is trained on public tweets, it is bound to recreate biases that people tweet. In order to load the model and tokenizer, run ``` from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets") model = AutoModelForMaskedLM.from_pretrained("justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets") ``` ## Training and evaluation data This model was further pre-trained on 300k tweets containing #covidvaccines from this [kaggle dataset](https://www.kaggle.com/kaushiksuresh147/covidvaccine-tweets). The evaluation set was 15% of the tweets that were held out from the training data. ## Training procedure See the training notebook found [here](). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.5775 | 1.0 | 8931 | 1.5852 | | 1.5715 | 2.0 | 17862 | 1.5701 | | 1.5394 | 3.0 | 26793 | 1.5089 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/test_emotion_trained_test
aXhyra
2021-12-12T17:23:27Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: test_emotion_trained_test results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: F1 type: f1 value: 0.7014611518188594 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_emotion_trained_test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5866 - F1: 0.7015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.458132814624325e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 51 | 0.7877 | 0.5569 | | No log | 2.0 | 102 | 0.6188 | 0.6937 | | No log | 3.0 | 153 | 0.5969 | 0.7068 | | No log | 4.0 | 204 | 0.5866 | 0.7015 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
huggingtweets/nolanfinleydn
huggingtweets
2021-12-12T17:01:02Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/nolanfinleydn/1639328457725/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1554982611/Nolan_Finley1_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nolan Finley</div> <div style="text-align: center; font-size: 14px;">@nolanfinleydn</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nolan Finley. | Data | Nolan Finley | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 1833 | | Short tweets | 49 | | Tweets kept | 1367 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2skb4p1f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nolanfinleydn's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pvvdm4g) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pvvdm4g/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nolanfinleydn') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
aXhyra/emotion_trained_1234567
aXhyra
2021-12-12T13:19:19Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: emotion_trained_1234567 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: F1 type: f1 value: 0.7301562209701973 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion_trained_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9051 - F1: 0.7302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6.961635072722524e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.6480 | 0.7231 | | No log | 2.0 | 408 | 0.6114 | 0.7403 | | 0.5045 | 3.0 | 612 | 0.7592 | 0.7311 | | 0.5045 | 4.0 | 816 | 0.9051 | 0.7302 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/emotion_trained_31415
aXhyra
2021-12-12T13:14:50Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: emotion_trained_31415 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: F1 type: f1 value: 0.719757533529152 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion_trained_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9274 - F1: 0.7198 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6.961635072722524e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.6177 | 0.7137 | | No log | 2.0 | 408 | 0.7489 | 0.6761 | | 0.5082 | 3.0 | 612 | 0.8233 | 0.7283 | | 0.5082 | 4.0 | 816 | 0.9274 | 0.7198 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/hate_trained_1234567
aXhyra
2021-12-12T13:02:26Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: hate_trained_1234567 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: hate metrics: - name: F1 type: f1 value: 0.7750768993843997 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hate_trained_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.7912 - F1: 0.7751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7272339744854407e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4835 | 1.0 | 563 | 0.4881 | 0.7534 | | 0.3236 | 2.0 | 1126 | 0.5294 | 0.7610 | | 0.219 | 3.0 | 1689 | 0.6095 | 0.7717 | | 0.1409 | 4.0 | 2252 | 0.7912 | 0.7751 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/hate_trained_31415
aXhyra
2021-12-12T12:57:50Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: hate_trained_31415 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: hate metrics: - name: F1 type: f1 value: 0.7729447444817463 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hate_trained_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8568 - F1: 0.7729 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7272339744854407e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.482 | 1.0 | 563 | 0.4973 | 0.7672 | | 0.3316 | 2.0 | 1126 | 0.4931 | 0.7794 | | 0.2308 | 3.0 | 1689 | 0.7073 | 0.7593 | | 0.1444 | 4.0 | 2252 | 0.8568 | 0.7729 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/hate_trained_42
aXhyra
2021-12-12T12:46:30Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: hate_trained_42 results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: hate metrics: - name: F1 type: f1 value: 0.7712319060633668 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hate_trained_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8994 - F1: 0.7712 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7272339744854407e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4835 | 1.0 | 563 | 0.4855 | 0.7556 | | 0.3277 | 2.0 | 1126 | 0.5354 | 0.7704 | | 0.2112 | 3.0 | 1689 | 0.6870 | 0.7751 | | 0.1384 | 4.0 | 2252 | 0.8994 | 0.7712 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
addy88/hubert-base-timit-demo-colab
addy88
2021-12-12T12:13:30Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "hubert", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: hubert-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hubert-base-timit-demo-colab This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1092 - Wer: 0.1728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.4664 | 4.0 | 500 | 2.3026 | 0.9866 | | 0.8171 | 8.0 | 1000 | 0.0980 | 0.1885 | | 0.2983 | 12.0 | 1500 | 0.0943 | 0.1750 | | 0.1769 | 16.0 | 2000 | 0.0990 | 0.1737 | | 0.1823 | 20.0 | 2500 | 0.1068 | 0.1757 | | 0.0761 | 24.0 | 3000 | 0.1041 | 0.1719 | | 0.0993 | 28.0 | 3500 | 0.1092 | 0.1728 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
harshit345/xlsr-53-wav2vec-hi
harshit345
2021-12-12T11:52:01Z
7
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "hi", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: hi datasets: - Interspeech 2021 metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Hindi by Shyam Sunder Kumar results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice hi type: common_voice args: hi metrics: - name: Test WER type: wer value: 20.22 --- # Wav2Vec2-Large-XLSR-53-hindi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "hi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the hindi test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "hi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**:20.22 % ## Training The script used for training can be found [Hindi ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1nY5WMj1oNlexD_qDeNYL7ZM427A021CV?usp=sharing)
aXhyra/hate_trained_final
aXhyra
2021-12-12T11:25:23Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: hate_trained_final results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: hate metrics: - name: F1 type: f1 value: 0.7697890540753396 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hate_trained_final This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5543 - F1: 0.7698 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.460503761236833e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.463 | 1.0 | 1125 | 0.5213 | 0.7384 | | 0.3943 | 2.0 | 2250 | 0.5134 | 0.7534 | | 0.3407 | 3.0 | 3375 | 0.5400 | 0.7666 | | 0.3121 | 4.0 | 4500 | 0.5543 | 0.7698 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
aXhyra/emotion_trained_final
aXhyra
2021-12-12T10:50:02Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: emotion_trained_final results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: F1 type: f1 value: 0.7469065445487402 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion_trained_final This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9349 - F1: 0.7469 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.502523631581398e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9013 | 1.0 | 815 | 0.7822 | 0.6470 | | 0.5008 | 2.0 | 1630 | 0.7142 | 0.7419 | | 0.3684 | 3.0 | 2445 | 0.8621 | 0.7443 | | 0.2182 | 4.0 | 3260 | 0.9349 | 0.7469 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
harshit345/wav2vec2-large-lv60-timit
harshit345
2021-12-11T22:38:44Z
6
1
transformers
[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "en", "dataset:timit_asr", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: en datasets: - timit_asr tags: - audio - automatic-speech-recognition - speech license: apache-2.0 --- # Wav2Vec2-Large-LV60-TIMIT Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor model_name = "hktayal345/wav2vec2-large-lv60-timit-asr" processor = Wav2Vec2Processor.from_pretrained(model_name) model = Wav2Vec2ForCTC.from_pretrained(model_name) model.eval() dataset = load_dataset("timit_asr", split="test").shuffle().select(range(10)) char_translations = str.maketrans({"-": " ", ",": "", ".": "", "?": ""}) def prepare_example(example): example["speech"], _ = sf.read(example["file"]) example["text"] = example["text"].translate(char_translations) example["text"] = " ".join(example["text"].split()) # clean up whitespaces example["text"] = example["text"].lower() return example dataset = dataset.map(prepare_example, remove_columns=["file"]) inputs = processor(dataset["speech"], sampling_rate=16000, return_tensors="pt", padding="longest") with torch.no_grad(): predicted_ids = torch.argmax(model(inputs.input_values).logits, dim=-1) predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id # see fine-tuning script predicted_transcripts = processor.tokenizer.batch_decode(predicted_ids) for reference, predicted in zip(dataset["text"], predicted_transcripts): print("reference:", reference) print("predicted:", predicted) print("--") ``` Here's the output: ``` reference: the emblem depicts the acropolis all aglow predicted: the amblum depicts the acropolis all a glo -- reference: don't ask me to carry an oily rag like that predicted: don't ask me to carry an oily rag like that -- reference: they enjoy it when i audition predicted: they enjoy it when i addition -- reference: set aside to dry with lid on sugar bowl predicted: set aside to dry with a litt on shoogerbowl -- reference: a boring novel is a superb sleeping pill predicted: a bor and novel is a suberb sleeping peel -- reference: only the most accomplished artists obtain popularity predicted: only the most accomplished artists obtain popularity -- reference: he has never himself done anything for which to be hated which of us has predicted: he has never himself done anything for which to be hated which of us has -- reference: the fish began to leap frantically on the surface of the small lake predicted: the fish began to leap frantically on the surface of the small lake -- reference: or certain words or rituals that child and adult go through may do the trick predicted: or certain words or rituals that child an adult go through may do the trick -- reference: are your grades higher or lower than nancy's predicted: are your grades higher or lower than nancies -- ``` ## Fine-Tuning Script You can find the script used to produce this model [here](https://colab.research.google.com/drive/1gVaZhFuIXxBDN2pD0esW490azlbQtQ7C?usp=sharing). **Note:** This model can be fine-tuned further; [trainer_state.json](https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/blob/main/trainer_state.json) shows useful details, namely the last state (this checkpoint): ```json { "epoch": 29.51, "eval_loss": 25.424150466918945, "eval_runtime": 182.9499, "eval_samples_per_second": 9.183, "eval_wer": 0.1351704233095107, "step": 8500 } ```
cylee/tutorial
cylee
2021-12-11T21:48:30Z
4
0
transformers
[ "transformers", "tf", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
# About This is a sample repo.