Datasets:

davanstrien

/

model_cards_with_readmes

like 1

priyankav/distilbert-base-uncased-finetuned-squad

priyankav

distilbert

transformers

question-answering

apache-2.0

['generated_from_keras_callback']

<!-- 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. --> # priyankavalappil/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9684 - Train End Logits Accuracy: 0.7305 - Train Start Logits Accuracy: 0.6893 - Validation Loss: 1.1278 - Validation End Logits Accuracy: 0.6999 - Validation Start Logits Accuracy: 0.6635 - Epoch: 1 ## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, '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} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.5059 | 0.6070 | 0.5685 | 1.1518 | 0.6816 | 0.6482 | 0 | | 0.9684 | 0.7305 | 0.6893 | 1.1278 | 0.6999 | 0.6635 | 1 | ### Framework versions - Transformers 4.22.2 - TensorFlow 2.8.2 - Datasets 2.5.2 - Tokenizers 0.12.1

f3a3cb874cea332c9a2c9049839bb772

tomekkorbak/competent_payne

tomekkorbak

mit

['en']

['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000']

['generated_from_trainer']

<!-- 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. --> # competent_payne This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - 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 - lr_scheduler_warmup_ratio: 0.01 - training_steps: 25000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True, 'skip_tokens': 1661599744}, 'generation': {'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': 'f9cb81e577effccc64697016af1e8eaf2bf5dcd2'}, 'path_or_name': 'tomekkorbak/nervous_wozniak'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'competent_payne', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1661599744, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2q5t671f

ed00b9106741e5ad8660d05d37de6a24

Helsinki-NLP/opus-mt-en-pag

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-en-pag * source languages: en * target languages: pag * OPUS readme: [en-pag](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-pag/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-pag/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-pag/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-pag/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.pag | 37.9 | 0.598 |

9ae798d149f22bf8b01357cfc3a95ea6

jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-5_belgium-5_s452

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'fr']

# exp_w2v2r_fr_xls-r_accent_france-5_belgium-5_s452 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

b3d98d644ab8812c3343b509df02a9f9

lmqg/mbart-large-cc25-frquad-qg

lmqg

mbart

transformers

text2text-generation

cc-by-4.0

['fr']

['lmqg/qg_frquad']

['question generation']

# Model Card of `lmqg/mbart-large-cc25-frquad-qg` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** fr - **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="fr", model="lmqg/mbart-large-cc25-frquad-qg") # model prediction questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="le Suprême Berger") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-frquad-qg") output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 71.48 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_1 | 14.36 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_2 | 3.58 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_3 | 1.45 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_4 | 0.72 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | METEOR | 7.78 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | MoverScore | 50.35 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | ROUGE_L | 16.4 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 81.27 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedF1Score (MoverScore) | 55.61 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedPrecision (BERTScore) | 81.29 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedPrecision (MoverScore) | 55.61 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedRecall (BERTScore) | 81.25 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedRecall (MoverScore) | 55.6 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mbart-large-cc25-frquad-ae`](https://huggingface.co/lmqg/mbart-large-cc25-frquad-ae). [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.lmqg_mbart-large-cc25-frquad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 75.55 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedF1Score (MoverScore) | 51.75 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedPrecision (BERTScore) | 74.04 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedPrecision (MoverScore) | 51.03 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedRecall (BERTScore) | 77.16 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedRecall (MoverScore) | 52.52 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoch: 8 - batch: 4 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

3ee1a395562e0b1e82f9abf439a5c516

jg/distilbert-base-uncased-finetuned-emotion

jg

distilbert

transformers

text-classification

apache-2.0

['emotion']

['generated_from_trainer']

<!-- 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2199 - F1: 0.9236 - Accuracy: 0.9235 ## 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: 64 - eval_batch_size: 64 - 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 | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 0.8072 | 1.0 | 250 | 0.3153 | 0.9023 | 0.905 | | 0.2442 | 2.0 | 500 | 0.2199 | 0.9236 | 0.9235 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

d13bff8d6b7152e704292d1f86863bbd

Cosk/sketchstyle-cutesexyrobutts

Cosk

diffusers

text-to-image

creativeml-openrail-m

['en']

['Cosk/cutesexyrobutts']

['stable-diffusion', 'art', 'cutesexyrobutts', 'style', 'dreambooth']

# 'Sketchstyle' (cutesexyrobutts style) Base model: https://huggingface.co/Linaqruf/anything-v3.0.</br> Used 'fast-DreamBooth' on Google Colab and 768x768 images for all versions. ## NEW: Merges *Merging sketchstyle models with other models will help to improve anatomy and other elements while also trying to keep the intended style as much as possible.</br> I will upload from time to time new merges, if any of those improves on the previous ones. </br> A 'weak' model means there is more weight to cutesexyrobutts style and a 'strong' model means there is a little more focus on the other model/models.</br> Weak models might mantain a little more of the style but could have some anatomy problems, while strong models keep better anatomy though the style might become a little affected. Low CFG Scale (5-9) and using the "sketchstyle" token in the prompts might help with keeping the style on strong models.</br>* **List of merges:** - Pastelmix 0.2 + sketchstyle_v4-42k 0.8 weak (weighted sum, fp16) - Pastelmix 0.4 + sketchstyle_v4-42k 0.6 strong (weighted sum, fp16) **Versions:** - V1: Trained with around 1300 images (from danbooru), automatically cropped. - V2: Trained with 400 handpicked and handcropped images. - V3: Trained with the same images as V2, but with 'style training' enabled. - V4: Trained with 407 images, including 'captions' for each image. **Recommended to use:** - V4-42k (pretty good style and decent anatomy, might be the best) - V3-40k (decent style and anatomy) - V4-10k (best anatomy, meh style) - V4-100k (good style, bad anatomy/hard to use, useful with img2img) **Usage recommendations:** - For V4, don't use CFG Scale over 11-12, as it will generate an overcooked image. Try between 6 to 9 at first. 9 seems to be the best if you're using the 'sketchstyle' in the prompt, if not, lower - Generating specific characters might be hard, result in bad anatomy or not even work at all. If you want an specific character, the best is to use img2img with an image generated with another model - Going over a certain resolution will generate incoherent results, so try staying close to 768x768 (examples: 640x896, 768x960, 640x1024, 832x640, and similar). Maybe Hires fix could help. - Make sure to add nsfw/nipples/huge or large breasts in the negative prompts if you don't want any of those. - Skin tone tends to be 'tan', use dark skin/tan on the negative prompts if its the case, and/or pale skin in the prompts. - Using img2img to change the style of another image generally gives the best results, examples below. Pay attention to this number. Normally going below 75 generates bad results, specially with models with high steps like V4-100k. Best with 100+  Token: 'sketchstyle' (if used, anatomy may get affected, but it can be useful for models with low steps to get a better style)<br /> **Limitations and known errors:** - Not very good anatomy - Sometimes it generates artifacts, specially on the eyes and lips - Tends to generate skimpy clothes, open clothes, cutouts, and similar - Might generate unclear outlines Try using inpainting and/or img2img to fix these. # Comparison between different versions and models As you can see, robutts tends to give less coherent results and might need more prompting/steps to get good results (tried on other things aswell with similar results)  V2 with 10k steps or lower tends to give better anatomy results, and over that the style appears more apparent, so 10k is the 'sweet spot'.  Around 40 steps seems to be the best, but you should use 20 steps and, if you get an image you like, you increase the step count to 40 or 50.  Comparison between not completing that negative prompt and increasing the strength too much.  Comparison (using V3-5k) of token strength.  Another comparison of token strength using V3-15k.  Comparison, from 1 to 30 steps, between NovelAI - Sketchstyle V3-27500 (img2img with NovelAI image) - Sketchstyle V3-27500. Using Euler sampler.  # Examples:  ```bibtex Prompt: (masterpiece,best quality,ultra-detailed),((((face close-up)))),((profile)),((lips,pink_eyes)),((pink_hair,hair_slicked_back,hair_strand)),(serious),portrait,frown,arms_up,adjusting_hair,eyelashes,parted_lips,(sportswear,crop_top),toned,collarbone,ponytail,1girl,solo,highres<br /> Negative prompt: (deformed,disfigured),(sitting,fat,thick,thick_thighs,nsfw),open_clothes,open_shirt,(jewelry,earrings,hair_ornament),((sagging_breasts,huge_breasts,shiny,shiny_hair,shiny_skin,realistic,3D,3D game)),((extra_limbs,extra_arms)),(loli,shota),(giant nipples),long body,(lowres),(((poorly drawn fingers, poorly drawn hands))),((anatomic nonsense)),(extra fingers),(fused fingers),(((one hand with more than 5 fingers))),(((one hand with less than 5 fingers))),(bad eyes),(separated eyes),(long neck),((bad proportions)),long body,((poorly drawn eyes)),((poorly drawn)),((bad drawing)),blurry,((mutation)),((bad anatomy)),(multiple arms),((bad face)),((bad eyes)),bad tail,((more than 2 ears)),((poorly drawn face)), (extra limb), ((deformed hands)), (poorly drawn feet), (mutated hands and fingers), extra legs, extra ears, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face maskissing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face mask<br /> Steps: 70, Sampler: Euler, CFG scale: 12, Seed: 1365838486, Size: 768x768, Model: Sketchstyle V3-5k ``` _Eyes fixed with inpainting_:  ```bibtex Prompt: (masterpiece,best quality,ultra-detailed),((((face close-up)))),((profile)),((lips,pink_eyes)),((pink_hair,hair_slicked_back,hair_strand)),(serious),portrait,frown,arms_up,adjusting_hair,eyelashes,parted_lips,(sportswear,crop_top),toned,collarbone,ponytail,1girl,solo,highres<br /> Negative prompt: (deformed,disfigured),(sitting,fat,thick,thick_thighs,nsfw),open_clothes,open_shirt,(jewelry,earrings,hair_ornament),((sagging_breasts,huge_breasts,shiny,shiny_hair,shiny_skin,realistic,3D,3D game)),((extra_limbs,extra_arms)),(loli,shota),(giant nipples),long body,(lowres),(((poorly drawn fingers, poorly drawn hands))),((anatomic nonsense)),(extra fingers),(fused fingers),(((one hand with more than 5 fingers))),(((one hand with less than 5 fingers))),(bad eyes),(separated eyes),(long neck),((bad proportions)),long body,((poorly drawn eyes)),((poorly drawn)),((bad drawing)),blurry,((mutation)),((bad anatomy)),(multiple arms),((bad face)),((bad eyes)),bad tail,((more than 2 ears)),((poorly drawn face)), (extra limb), ((deformed hands)), (poorly drawn feet), (mutated hands and fingers), extra legs, extra ears, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face maskissing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face mask<br /> Steps: 34, Sampler: Euler, CFG scale: 12, Seed: 996011741, Size: 768x768, Denoising strength: 0.6, Mask blur: 8, Model: Sketchstyle V2-10k ```  ```bibtex Prompt: sketchstyle,(masterpiece, best quality,beautiful lighting,stunning,ultra-detailed),(portrait,upper_body,parted_lips),1girl, (nipples), (fox ears,animal_ear_fluff), (bare_shoulders,eyelashes,lips,orange eyes,blush),orange_hair,((onsen,indoors)),(toned),medium_breasts,navel,cleavage,looking at viewer,collarbone,hair bun, solo, highres,(nsfw)<br /> Negative prompt: (dark-skin,dark_nipples,extra_nipples),deformed,disfigured,(sitting,fat,thick,thick_thighs,nsfw),open_clothes,open_shirt,(jewelry,earrings,hair_ornament),((sagging_breasts,huge_breasts,shiny,shiny_hair,shiny_skin,realistic,3D,3D game)),((extra_limbs,extra_arms)),(loli,shota),(giant nipples),long body,(lowres),(((poorly drawn fingers, poorly drawn hands))),((anatomic nonsense)),(extra fingers),(fused fingers),(((one hand with more than 5 fingers))),(((one hand with less than 5 fingers))),(bad eyes),(separated eyes),(long neck),((bad proportions)),long body,((poorly drawn eyes)),((poorly drawn)),((bad drawing)),blurry,((mutation)),((bad anatomy)),(multiple arms),((bad face)),((bad eyes)),bad tail,((more than 2 ears)),((poorly drawn face)), (extra limb), ((deformed hands)), (poorly drawn feet), (mutated hands and fingers), extra legs, extra ears, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face maskissing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face mask<br /> Steps: 30, Sampler: Euler, CFG scale: 12, Seed: 4172541433, Size: 640x832, Model: Sketchstyle V3-5k ```  ```bibtex Prompt: sketchstyle,(masterpiece, best quality,beautiful lighting,stunning,ultra-detailed),(portrait,upper_body),1girl, (nipples), (fox ears,animal_ear_fluff), (bare_shoulders,eyelashes,lips,orange eyes,ringed_eyes,shy,blush),onsen,indoors,medium_breasts, cleavage,looking at viewer,collarbone,hair bun, solo, highres,(nsfw)<br /> Negative prompt: Negative prompt: (huge_breasts,large_breasts),realistic,3D,3D Game,nsfw,lowres, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth<br /> Steps: 40, Sampler: Euler, CFG scale: 14, Seed: 4268937236, Size: 704x896, Model: Sketchstyle V3-5k ```  ```bibtex Prompt: (masterpiece,best quality,ultra detailed),(((facing_away,sitting,arm_support,thighs,legs))),(((from_behind,toned,ass,bare back,breasts))),((thong,garter_belt,garter_straps,lingerie)),(hair_flower,bed_sheet),(black_hair,braid,braided_ponytail,long_hair),1girl,grey_background,thighs,solo,highres<br /> Negative prompt: ((deformed)),((looking_back,looking_at_viewer,face)),((out_of_frame,cropped)),(fat,thick,thick_thighs),((sagging_breasts,huge_breasts,shiny,shiny_hair,shiny_skin,3D,3D game)),((extra_limbs,extra_arms)),(loli,shota),(giant nipples),long body,(lowres),(((poorly drawn fingers, poorly drawn hands))),((anatomic nonsense)),(extra fingers),(fused fingers),(((one hand with more than 5 fingers))),(((one hand with less than 5 fingers))),(bad eyes),(separated eyes),(long neck),((bad proportions)),long body,((poorly drawn eyes)),((poorly drawn)),((bad drawing)),blurry,((mutation)),((bad anatomy)),(multiple arms),((bad face)),((bad eyes)),bad tail,((more than 2 ears)),((poorly drawn face)), (extra limb), ((deformed hands)), (poorly drawn feet), (mutated hands and fingers), extra legs, extra ears, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, patreon_logo, patreon_username, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face maskissing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face mask<br /> Steps: 50, Sampler: Euler, CFG scale: 12, Seed: 3765393440, Size: 640x832, Model: Sketchstyle V3-5k ```  ```bibtex Prompt: (masterpiece,best quality,ultra detailed),(((facing_away,sitting,arm_support,thighs,legs))),(((from_behind,toned,ass,bare back))),((thong,garter_belt,garter_straps,lingerie)),(hair_flower,bed_sheet),(black_hair,braid,braided_ponytail,long_hair),1girl,grey_background,thighs,solo,highres<br /> Negative prompt: backboob,((deformed)),((looking_back,looking_at_viewer,face)),((out_of_frame,cropped)),(fat,thick,thick_thighs),((sagging_breasts,huge_breasts,shiny,shiny_hair,shiny_skin,3D,3D game)),((extra_limbs,extra_arms)),(loli,shota),(giant nipples),long body,(lowres),(((poorly drawn fingers, poorly drawn hands))),((anatomic nonsense)),(extra fingers),(fused fingers),(((one hand with more than 5 fingers))),(((one hand with less than 5 fingers))),(bad eyes),(separated eyes),(long neck),((bad proportions)),long body,((poorly drawn eyes)),((poorly drawn)),((bad drawing)),blurry,((mutation)),((bad anatomy)),(multiple arms),((bad face)),((bad eyes)),bad tail,((more than 2 ears)),((poorly drawn face)), (extra limb), ((deformed hands)), (poorly drawn feet), (mutated hands and fingers), extra legs, extra ears, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, patreon_logo, patreon_username, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face maskissing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face mask<br /> Steps: 50, Sampler: Euler, CFG scale: 12, Seed: 2346086519, Size: 640x832, Model: Sketchstyle V3-5k ```  ```bibtex Prompt: (masterpiece,best quality,ultra-detailed),(sketchstyle),(arms_up,tying_hair),(large_breasts,nipples),(long_hair,blonde_hair,tied_hair,ponytail,collarbone,navel,stomach,midriff,completely_nude,nude,toned),((cleft_of_venus,pussy)),cloudy_sky,1girl,solo,highres,(nsfw)<br /> Negative prompt: (deformed,disfigured,bad proportions,exaggerated),from_behind,(jewelry,earrings,hair_ornament),((sagging_breasts,huge_breasts,shiny,shiny_hair,shiny_skin,realistic,3D,3D game)),((extra_limbs,extra_arms)),(loli,shota),(giant nipples),((fat,thick,thick_thighs)),long body,(lowres),(((poorly drawn fingers, poorly drawn hands))),((anatomic nonsense)),(extra fingers),(fused fingers),(((one hand with more than 5 fingers))),(((one hand with less than 5 fingers))),(bad eyes),(separated eyes),(long neck),((bad proportions)),long body,((poorly drawn eyes)),((poorly drawn)),((bad drawing)),blurry,((mutation)),((bad anatomy)),(multiple arms),((bad face)),((bad eyes)),bad tail,((more than 2 ears)),((poorly drawn face)), (extra limb), ((deformed hands)), (poorly drawn feet), (mutated hands and fingers), extra legs, extra ears, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face maskissing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face mask<br /> Steps: 40, Sampler: Euler, CFG scale: 12, Seed: 4024165718, Size: 704x960, Model: Sketchstyle V3-5k ```  ```bibtex Prompt: (masterpiece,best quality),(sketchstyle),((1boy,male_focus)),((close-up,portrait)),((black_shirt)),((((red collared_coat)))),((dante_\(devil_may_cry\),devil may cry)),((medium_hair,parted_hair,parted_bangs,forehead,white_hair)),((stubble)),(facial_hair),(popped_collar,open_coat),(closed_mouth,smile),blue_eyes,looking_at_viewer,solo,highres<br /> Negative prompt: ((deformed)),(nsfw),(long_hair,short_hair,young,genderswap,1girl,female,breasts,androgynous),((choker)),(shiny,shiny_hair,shiny_skin,3D,3D game),((extra_limbs,extra_arms)),(loli,shota),(giant nipples),((fat,thick,thick_thighs)),long body,(lowres),(((poorly drawn fingers, poorly drawn hands))),((anatomic nonsense)),(extra fingers),(fused fingers),(((one hand with more than 5 fingers))),(((one hand with less than 5 fingers))),(bad eyes),(separated eyes),(long neck),((bad proportions)),long body,((poorly drawn eyes)),((poorly drawn)),((bad drawing)),blurry,((mutation)),((bad anatomy)),(multiple arms),((bad face)),((bad eyes)),bad tail,((more than 2 ears)),((poorly drawn face)), (extra limb), ((deformed hands)), (poorly drawn feet), (mutated hands and fingers), extra legs, extra ears, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face maskissing fingers, bad hands, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled,monochrome, greyscale,face mask<br /> Steps: 50, Sampler: Euler, CFG scale: 12, Seed: 4166887955, Size: 768x768, Model: Sketchstyle V3-5k ``` # img2img style change examples:  ```bibtex Original settings: Model: NovelAI, Steps: 30, Sampler: Euler a, CFG scale: 16, Seed: 3633297035, Size: 640x960<br /> Original prompt: masterpiece, best quality, 1girl, naked towel, fox ears, orange eyes, wet, ringed eyes, shy, medium breasts, cleavage, looking at viewer, hair bun, blush, solo, highres<br /> Original negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth<br /> New settings: Model: Sketchstyle V3 5k steps, Steps: 33, CFG scale: 12, Seed: 3311014108, Size: 640x960, Denoising strength: 0.6, Mask blur: 4<br /> New prompt: ((sketchstyle)),(masterpiece, best quality,beautiful lighting,stunning,ultra-detailed),(portrait,upper_body),1girl, (((naked_towel,towel))), (fox ears,animal_ear_fluff), (bare_shoulders,eyelashes,lips,orange eyes,ringed_eyes,shy,blush),onsen,indoors,medium_breasts, cleavage,looking at viewer,collarbone,hair bun, solo, highres<br /> New negative prompt: (nipples,huge_breasts,large_breasts),realistic,3D,3D Game,nsfw,lowres, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth<br /> ```  ```bibtex Original settings: Model: NovelAI, Steps: 30, Sampler: Euler a, CFG scale: 16, Seed: 764529639, Size: 640x960<br /> Prompt: masterpiece, highest quality, (1girl), (looking at viewer), ((pov)), fox ears, ((leaning forward)), [light smile], ((camisole)), short shorts, (cleavage), (((medium breasts))), blonde, (high ponytail), (highres)<br /> Negative prompt: ((deformed)), (duplicated), lowres, ((missing animal ears)), ((poorly drawn face)), ((poorly drawn eyes)), (extra limb), (mutation), ((deformed hands)), (((poorly drawn hands))), (poorly drawn feet), (fused toes), (fused fingers), (mutated hands and fingers), (one hand with more than 5 fingers), (one hand with less than 5 fingers), extra toes, missing toes, extra feet, extra legs, extra ears, missing ear, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled, huge breasts, black and white, monochrome, 3D Game, 3D, realistic, realism, huge breasts<br /> New settings: Model: Sketchstyle V3 5k steps, Steps: 28, CFG scale: 12, Seed: 1866024520, Size: 640x960, Denoising strength: 0.7, Mask blur: 8 ```  ```bibtex Original settings: Model: NovelAI, Steps: 25, Sampler: Euler a, CFG scale: 11, Seed: 2604970030, Size: 640x896<br /> Original prompt: (masterpiece),(best quality),((sketch)),(ultra detailed),(1girl, teenage),((white hair, messy hair)),((expressionless)),(black jacket, long sleeves),((grey scarf)),((squatting)), (hands on own knees),((plaid_skirt, pleated skirt, miniskirt)),(fox ears, extra ears, white fox tail, fox girl, animal ear fluff),black ((boots)),full body,bangs,ahoge,(grey eyes),solo,absurdres<br /> Negative prompt: ((deformed)),((loli, young)),(kneehighs,thighhighs),long body, long legs),lowres,((((poorly drawn fingers, poorly drawn hands)))),((anatomic nonsense)),(extra fingers),((fused fingers)),(plaid scarf),(spread legs),((one hand with more than 5 fingers)), ((one hand with less than 5 fingers)),((bad eyes)),(twin, multiple girls, 2girls),(separated eyes),(long neck),((bad proportions)),(bad lips),((thick lips)),loli,long body,(((poorly drawn eyes))),((poorly drawn)),((bad drawing)),(blurry),(((mutation))),(((bad anatomy))),(((multiple arms))),(((bad face))),(((bad eyes))),bad tail,(((more than 2 ears)), (((poorly drawn face))), (extra limb), ((deformed hands)), (poorly drawn feet), (fused toes), (mutated hands and fingers), extra toes, missing toes, extra feet, extra legs, extra ears, missing ear, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled, huge breasts, black and white, monochrome, 3D Game, 3D, (realistic), face mask<br /> New settings: Model: Sketchstyle V3 5k steps, Steps: 45, CFG scale: 12, Seed: 1073378414, Size: 640x896, Denoising strength: 0.6, Mask blur: 8<br /> New prompt: (masterpiece),(best quality),(sketchstyle),(ultra detailed),(1girl, teenage),((white hair, messy hair)),((expressionless)),(black jacket, long sleeves),((grey scarf)),((squatting)), (hands on own knees),((plaid_skirt, pleated skirt, miniskirt)),(fox ears, extra ears, white fox tail, fox girl, animal ear fluff),black ((boots)),full body,bangs,ahoge,(grey eyes),solo,absurdres<br /> ```  ```bibtex Original settings: Model: NovelAI, Steps: 30, Sampler: Euler a, CFG scale: 12, Seed: 3659534337, Size: 768x832<br /> Original prompt: ((masterpiece)), ((highest quality)),(((ultra-detailed))),(illustration),(1girl), portrait,((wolf ears)),(beautiful eyes),looking at viewer,dress shirt,shadows,((ponytail)), (white hair), ((sidelocks)),outdoors,bangs, solo, highres<br /> Original negative prompt: ((deformed)), lowres,loli,((monochrome)),(black and white),((lips)),long body,(((poorly drawn eyes))),((out of frame)),((poorly drawn)),((bad drawing)),(blurry),depth of field,(fused fingers),(((mutation))),((bad anatomy)),(((multiple arms))),(((bad face))),(((bad eyes))),bad tail,(((more than 2 ears)), (((poorly drawn face))), (extra limb), ((deformed hands)), (((poorly drawn hands))), (poorly drawn feet), (fused toes), (mutated hands and fingers), (one hand with more than 5 fingers), (one hand with less than 5 fingers), extra toes, missing toes, extra feet, extra legs, extra ears, missing ear, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled, huge breasts, black and white, monochrome, 3D Game, 3D, realism, face mask<br /> New settings: Model: Sketchstyle V3-20k 2000steps text encoder, Steps: 80, CFG scale: 12, Seed: 3001145714, Size: 768x832, Denoising strength: 0.5, Mask blur: 4<br /> New prompt: ((sketchstyle)),(masterpiece,best quality,highest quality,illustration),((ultra-detailed)),1girl,(portrait,close-up),((wolf_girl,wolf_ears)),(eyelashes,detailed eyes,beautiful eyes),looking at viewer,(collared-shirt,white_shirt),((ponytail)), (white hair), ((sidelocks)),(blue eyes),closed_mouth,(shadows,outdoors,sunlight,grass,trees),hair_between_eyes,bangs,solo,highres<br /> New negative prompt: ((deformed)),(less than 5 fingers, more than 5 fingers,bad hands,bad hand anatomy,missing fingers, extra fingers, mutated hands, disfigured hands, deformed hands),lowres,loli,((monochrome)),(black and white),((lips)),long body,(((poorly drawn eyes))),((out of frame)),((poorly drawn)),((bad drawing)),(blurry),depth of field,(fused fingers),(((mutation))),((bad anatomy)),(((multiple arms))),(((bad face))),(((bad eyes))),bad tail,(((more than 2 ears)), (((poorly drawn face))), (extra limb), ((deformed hands)), (((poorly drawn hands))), (poorly drawn feet), (fused toes), (mutated hands and fingers), (one hand with more than 5 fingers), (one hand with less than 5 fingers), extra toes, missing toes, extra feet, extra legs, extra ears, missing ear, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth, animal hands, censored, blurry lines, wacky outlines, unclear outlines, doubled, huge breasts, black and white, monochrome, 3D Game, 3D, realism, face mask<br /> ```  ```bibtex Original settings: Model: NovelAI, Steps: 20, Sampler: Euler, CFG scale: 11, Seed: 2413712316, Size: 768x768<br /> Original prompt: (masterpiece,best quality,ultra-detailed,detailed_eyes),(sketch),((portrait,face focus)),(((shaded eyes))),(wavy hair),(((ringed eyes,red_hair))),((black hair ribbon)),((hair behind ear)),(((short ponytail))),(blush lines),(good anatomy),(((hair strands))),(bangs),((lips)),[teeth, tongue],yellow eyes,(eyelashes),shirt, v-neck,collarbone,cleavage,breasts,(medium hair),(sidelocks),looking at viewer,(shiny hair),1girl,solo,highres<br /> Original negative prompt: ((deformed)),lowres,(black hair),(formal),earrings,(twin, multiple girls, 2girls),(braided bangs),((big eyes)),((close up, eye focus)),(separated eyes),(multiple eyebrows),((eyebrows visible through hair)),(long neck),(bad lips),(tongue out),((thick lips)),(from below),loli,long body,(((poorly drawn eyes))),((poorly drawn)),((bad drawing)),((blurry)),depth of field,(fused fingers),(((mutation))),(((bad anatomy))),(((multiple arms))),(((bad face))),(((bad eyes))),bad tail,(((more than 2 ears)), (((poorly drawn face))), (extra limb), ((deformed hands)), (((poorly drawn hands))), (poorly drawn feet), (fused toes), (mutated hands and fingers), (one hand with more than 5 fingers), (one hand with less than 5 fingers), extra toes, missing toes, extra feet, extra legs, extra ears, missing ear, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth, animal hands, censored,doubled, huge breasts, black and white, monochrome, 3D Game, 3D, (realistic), face mask<br /> New settings: (img2img with original image, then again with the new generated image, inpainted to fix the neck) Model: Sketchstyle V3-27.5k 2000steps text encoder, Steps: 80, CFG scale: 12, Seed: 1237755461 / 1353966202, Size: 832x832, Denoising strength: 0.5 / 0.3, Mask blur: 4<br /> New prompt: sketchstyle,(masterpiece,best quality,ultra-detailed,detailed_eyes),(((portrait,face focus,close-up))),(((shaded eyes))),(wavy hair),(((ringed eyes,red_hair))),((black hair ribbon)),((hair behind ear)),(((short ponytail))),(blush lines),(good anatomy),(((hair strands))),(bangs),((lips)),[teeth, tongue],(yellow eyes,eyelashes,tsurime,slanted_eyes),shirt, v-neck,collarbone,breasts,(medium hair),(sidelocks),looking at viewer,(shiny hair),1girl,solo,highres<br /> New negative prompt: ((deformed)),((loli,young)),lowres,(black hair),(formal),earrings,(twin, multiple girls, 2girls),(braided bangs),((big eyes)),((close up, eye focus)),(separated eyes),(multiple eyebrows),((eyebrows visible through hair)),(long neck),(bad lips),(tongue out),((thick lips)),(from below),loli,long body,(((poorly drawn eyes))),((poorly drawn)),((bad drawing)),((blurry)),depth of field,(fused fingers),(((mutation))),(((bad anatomy))),(((multiple arms))),(((bad face))),(((bad eyes))),bad tail,(((more than 2 ears)), (((poorly drawn face))), (extra limb), ((deformed hands)), (((poorly drawn hands))), (poorly drawn feet), (fused toes), (mutated hands and fingers), (one hand with more than 5 fingers), (one hand with less than 5 fingers), extra toes, missing toes, extra feet, extra legs, extra ears, missing ear, extra hands, bad feet, bad anatomy, bad hands, text, error, missing fingers, bad hands, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, bad face, bad mouth, animal hands, censored,doubled, huge breasts, black and white, monochrome, 3D Game, 3D, (realistic), face mask<br /> ```

dc337dec4b1704b0d68424945b50efc2

sania-nawaz/finetuning-sentiment-model-3000-samples

sania-nawaz

distilbert

transformers

text-classification

apache-2.0

['imdb']

['generated_from_trainer']

<!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3286 - Accuracy: 0.8667 - F1: 0.8667 ## 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 ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

41729641147facf0eb8fb402e6b9305e

pkachhad/t5-base-finetuned-parth

pkachhad

t5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- 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. --> # t5-base-finetuned-parth This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3764 - Rouge1: 27.5144 - Rouge2: 22.6391 - Rougel: 25.9369 - Rougelsum: 27.1193 - Gen Len: 17.5 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 4 | 2.7016 | 27.6196 | 22.7595 | 25.9443 | 27.2369 | 17.5 | | No log | 2.0 | 8 | 2.5425 | 27.6196 | 22.7595 | 25.9443 | 27.2369 | 17.5 | | No log | 3.0 | 12 | 2.4526 | 27.6196 | 22.7595 | 25.9443 | 27.2369 | 17.5 | | No log | 4.0 | 16 | 2.3977 | 27.6196 | 22.7595 | 25.9443 | 27.2369 | 17.5 | | No log | 5.0 | 20 | 2.3764 | 27.5144 | 22.6391 | 25.9369 | 27.1193 | 17.5 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

2a2f6e3e6b27090868e8daef9b03f68e

RASMUS/wav2vec2-xlsr-fi-lm-1B

RASMUS

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['fi']

['generated_from_trainer', 'automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard']

<!-- 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-xlsr-fi-lm-1B This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common voice train/dev/other datasets. It achieves the following results on the evaluation set without language model: - Loss: 0.1853 - Wer: 0.2205 With language model: - Wer: 0.1026 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8158 | 0.67 | 400 | 0.4835 | 0.6310 | | 0.5679 | 1.33 | 800 | 0.4806 | 0.5538 | | 0.6055 | 2.0 | 1200 | 0.3888 | 0.5083 | | 0.5353 | 2.67 | 1600 | 0.3258 | 0.4365 | | 0.4883 | 3.33 | 2000 | 0.3313 | 0.4204 | | 0.4513 | 4.0 | 2400 | 0.2924 | 0.3904 | | 0.3753 | 4.67 | 2800 | 0.2593 | 0.3608 | | 0.3478 | 5.33 | 3200 | 0.2832 | 0.3551 | | 0.3796 | 6.0 | 3600 | 0.2495 | 0.3402 | | 0.2556 | 6.67 | 4000 | 0.2342 | 0.3106 | | 0.229 | 7.33 | 4400 | 0.2181 | 0.2812 | | 0.205 | 8.0 | 4800 | 0.2041 | 0.2523 | | 0.1654 | 8.67 | 5200 | 0.2015 | 0.2416 | | 0.152 | 9.33 | 5600 | 0.1942 | 0.2294 | | 0.1569 | 10.0 | 6000 | 0.1853 | 0.2205 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

56db68be00fa9d3a682990b7fca040bb

Tristan/gpt2-summarization_reward_model

Tristan

mit

['generated_from_trainer']

<!-- 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. --> # gpt2-summarization_reward_model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7473 - Accuracy: 0.6006 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 64 - total_eval_batch_size: 64 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6421 | 1.0 | 1451 | 0.6815 | 0.6036 | | 0.5893 | 2.0 | 2902 | 0.6764 | 0.6048 | | 0.5488 | 3.0 | 4353 | 0.7074 | 0.6012 | | 0.5187 | 4.0 | 5804 | 0.7254 | 0.6009 | | 0.5034 | 5.0 | 7255 | 0.7473 | 0.6006 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

ce6b6a8d7014d4b2f3ed60fee98348a3

deepset/deberta-v3-large-squad2

deepset

deberta-v2

transformers

question-answering

cc-by-4.0

['en']

['squad_v2']

['deberta', 'deberta-v3', 'deberta-v3-large']

# deberta-v3-large for QA This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview **Language model:** deberta-v3-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 1x NVIDIA A10G ## Hyperparameters ``` batch_size = 2 grad_acc_steps = 32 n_epochs = 6 base_LM_model = "microsoft/deberta-v3-large" max_seq_len = 512 learning_rate = 7e-6 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/deberta-v3-large-squad2") # or reader = TransformersReader(model_name_or_path="deepset/deberta-v3-large-squad2",tokenizer="deepset/deberta-v3-large-squad2") ``` ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/deberta-v3-large-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 87.6105449338836, "f1": 90.75307008866517, "total": 11873, "HasAns_exact": 84.37921727395411, "HasAns_f1": 90.6732795483674, "HasAns_total": 5928, "NoAns_exact": 90.83263246425568, "NoAns_f1": 90.83263246425568, "NoAns_total": 5945 ``` ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)

368c912f4fcdfaea2940d24c924d3103

gchhablani/fnet-base-finetuned-rte

gchhablani

fnet

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer', 'fnet-bert-base-comparison']

<!-- 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. --> # fnet-base-finetuned-rte This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6978 - Accuracy: 0.6282 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6829 | 1.0 | 156 | 0.6657 | 0.5704 | | 0.6174 | 2.0 | 312 | 0.6784 | 0.6101 | | 0.5141 | 3.0 | 468 | 0.6978 | 0.6282 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

c5e18835dae5d80019789c5da98f74da

Amir13/xlm-roberta-base-fa-base-ner

Amir13

xlm-roberta

transformers

token-classification

mit

['generated_from_trainer']

<!-- 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-fa-base-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2856 - Precision: 0.5353 - Recall: 0.5704 - F1: 0.5523 - Accuracy: 0.9168 ## 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.673 | 1.0 | 511 | 0.4067 | 0.4767 | 0.3981 | 0.4339 | 0.8956 | | 0.3673 | 2.0 | 1022 | 0.3279 | 0.4611 | 0.5138 | 0.4860 | 0.9031 | | 0.2998 | 3.0 | 1533 | 0.2977 | 0.5265 | 0.4976 | 0.5116 | 0.9132 | | 0.2616 | 4.0 | 2044 | 0.2860 | 0.5365 | 0.5477 | 0.5420 | 0.9151 | | 0.2394 | 5.0 | 2555 | 0.2856 | 0.5353 | 0.5704 | 0.5523 | 0.9168 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

8cda5835de7932af3aaa6c3e2ea70e5b

PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus

PlanTL-GOB-ES

roberta

transformers

token-classification

apache-2.0

['es']

['bne', 'capitel']

['national library of spain', 'spanish', 'bne', 'capitel', 'ner']

# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. ## Table of contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Evaluation](#evaluation) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description The **roberta-base-bne-capitel-ner-plus** is a Named Entity Recognition (NER) model for the Spanish language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. This model is a more robust version of the [roberta-base-bne-capitel-ner](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner) model that recognizes better lowercased Named Entities (NE). ## Intended uses and limitations **roberta-base-bne-capitel-ner-plus** model can be used to recognize Named Entities (NE). The model is limited by its training dataset and may not generalize well for all use cases. ## How to use ```python from transformers import pipeline from pprint import pprint nlp = pipeline("ner", model="PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus") example = "Me llamo francisco javier y vivo en madrid." ner_results = nlp(example) pprint(ner_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training The dataset used for training and evaluation is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1). We lowercased and uppercased the dataset, and added the additional sentences to the training. ### Training procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 score. ## Evaluation results We evaluated the **roberta-base-bne-capitel-ner-plus** on the CAPITEL-NERC test set against standard multilingual and monolingual baselines: | Model | CAPITEL-NERC (F1) | | ------------|:----| | roberta-large-bne-capitel-ner | **90.51** | | roberta-base-bne-capitel-ner | 89.60| | roberta-base-bne-capitel-ner-plus | 89.60| | BETO | 87.72 | | mBERT | 88.10 | | BERTIN | 88.56 | | ELECTRA | 80.35 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) ### Contact information For further information, send an email to <plantl-gob-es@bsc.es> ### Copyright Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. ### Citing information If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405): ``` @article{, abstract = {We want to thank the National Library of Spain for such a large effort on the data gathering and the Future of Computing Center, a Barcelona Supercomputing Center and IBM initiative (2020). This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.}, author = {Asier Gutiérrez Fandiño and Jordi Armengol Estapé and Marc Pàmies and Joan Llop Palao and Joaquin Silveira Ocampo and Casimiro Pio Carrino and Carme Armentano Oller and Carlos Rodriguez Penagos and Aitor Gonzalez Agirre and Marta Villegas}, doi = {10.26342/2022-68-3}, issn = {1135-5948}, journal = {Procesamiento del Lenguaje Natural}, keywords = {Artificial intelligence,Benchmarking,Data processing.,MarIA,Natural language processing,Spanish language modelling,Spanish language resources,Tractament del llenguatge natural (Informàtica),Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural}, publisher = {Sociedad Española para el Procesamiento del Lenguaje Natural}, title = {MarIA: Spanish Language Models}, volume = {68}, url = {https://upcommons.upc.edu/handle/2117/367156#.YyMTB4X9A-0.mendeley}, year = {2022}, } ``` ### Disclaimer The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models. Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables. Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial. En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.

9148fe94a4a3e68666ef89340987d3e1

gustavecortal/camembert-base-cae-pensees

gustavecortal

camembert

transformers

text-classification

mit

['generated_from_trainer']

<!-- 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. --> # camembert-base-cae-ressentis-pensees This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6742 - Precision: 0.8398 - Recall: 0.8417 - F1: 0.8384 ## 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: 8 - 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_ratio: 0.1 - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.2705 | 1.0 | 60 | 1.1442 | 0.2844 | 0.5333 | 0.3710 | | 1.0289 | 2.0 | 120 | 0.8098 | 0.8284 | 0.75 | 0.7533 | | 0.6056 | 3.0 | 180 | 0.5520 | 0.8345 | 0.8167 | 0.8042 | | 0.3228 | 4.0 | 240 | 0.5299 | 0.8198 | 0.825 | 0.8181 | | 0.1346 | 5.0 | 300 | 0.7416 | 0.8067 | 0.8083 | 0.7944 | | 0.0518 | 6.0 | 360 | 0.6852 | 0.8330 | 0.8333 | 0.8226 | | 0.0356 | 7.0 | 420 | 0.6758 | 0.8416 | 0.8417 | 0.8373 | | 0.0221 | 8.0 | 480 | 0.6996 | 0.8300 | 0.8333 | 0.8299 | | 0.0161 | 9.0 | 540 | 0.6701 | 0.8398 | 0.8417 | 0.8384 | | 0.0145 | 10.0 | 600 | 0.6742 | 0.8398 | 0.8417 | 0.8384 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.0 - Tokenizers 0.13.1

34e2d6d7ed3945ccdf6994b3e7309843

shmuhammad/distilbert-base-uncased-distilled-clinc

shmuhammad

distilbert

transformers

text-classification

apache-2.0

['clinc_oos']

['generated_from_trainer']

<!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3060 - Accuracy: 0.9487 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.643 | 1.0 | 318 | 1.9110 | 0.7452 | | 1.4751 | 2.0 | 636 | 0.9678 | 0.8606 | | 0.7736 | 3.0 | 954 | 0.5578 | 0.9168 | | 0.4652 | 4.0 | 1272 | 0.4081 | 0.9352 | | 0.3364 | 5.0 | 1590 | 0.3538 | 0.9442 | | 0.2801 | 6.0 | 1908 | 0.3294 | 0.9465 | | 0.2515 | 7.0 | 2226 | 0.3165 | 0.9471 | | 0.2366 | 8.0 | 2544 | 0.3107 | 0.9487 | | 0.2292 | 9.0 | 2862 | 0.3069 | 0.9490 | | 0.2247 | 10.0 | 3180 | 0.3060 | 0.9487 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1.post200 - Datasets 1.16.1 - Tokenizers 0.10.3

732f595b8e604320b96a7d674a78b22c

patrickvonplaten/led-large-16384-pubmed

patrickvonplaten

led

transformers

text2text-generation

apache-2.0

['en']

['scientific_papers']

[]

## Introduction [Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer). This is an unofficial *led-large-16384* checkpoint that is fine-tuned on the [pubmed dataset](https://huggingface.co/datasets/scientific_papers). The model was fine-tuned and evaluated as detailed in [this notebook](https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing) ## Results The model achieves a **Rouge-2** score of 19.33 on Pubmed which is competitive to state-of-the-art models. ## Usage The model can be used as follows. The input is taken from the test data of the [pubmed dataset](https://huggingface.co/datasets/scientific_papers). ```python LONG_ARTICLE = """"anxiety affects quality of life in those living with parkinson 's disease ( pd ) more so than overall cognitive status , motor deficits , apathy , and depression [ 13 ] . although anxiety and depression are often related and coexist in pd patients , recent research suggests that anxiety rather than depression is the most prominent and prevalent mood disorder in pd [ 5 , 6 ] . yet , our current understanding of anxiety and its impact on cognition in pd , as well as its neural basis and best treatment practices , remains meager and lags far behind that of depression . overall , neuropsychiatric symptoms in pd have been shown to be negatively associated with cognitive performance . for example , higher depression scores have been correlated with lower scores on the mini - mental state exam ( mmse ) [ 8 , 9 ] as well as tests of memory and executive functions ( e.g. , attention ) [ 1014 ] . likewise , apathy and anhedonia in pd patients have been associated with executive dysfunction [ 10 , 1523 ] . however , few studies have specifically investigated the relationship between anxiety and cognition in pd . one study showed a strong negative relationship between anxiety ( both state and trait ) and overall cognitive performance ( measured by the total of the repeatable battery for the assessment of neuropsychological status index ) within a sample of 27 pd patients . furthermore , trait anxiety was negatively associated with each of the cognitive domains assessed by the rbans ( i.e. , immediate memory , visuospatial construction , language , attention , and delayed memory ) . two further studies have examined whether anxiety differentially affects cognition in patients with left - sided dominant pd ( lpd ) versus right - sided dominant pd ( rpd ) ; however , their findings were inconsistent . the first study found that working memory performance was worse in lpd patients with anxiety compared to rpd patients with anxiety , whereas the second study reported that , in lpd , apathy but not anxiety was associated with performance on nonverbally mediated executive functions and visuospatial tasks ( e.g. , tmt - b , wms - iii spatial span ) , while in rpd , anxiety but not apathy significantly correlated with performance on verbally mediated tasks ( e.g. , clock reading test and boston naming test ) . furthermore , anxiety was significantly correlated with neuropsychological measures of attention and executive and visuospatial functions . taken together , it is evident that there are limited and inconsistent findings describing the relationship between anxiety and cognition in pd and more specifically how anxiety might influence particular domains of cognition such as attention and memory and executive functioning . it is also striking that , to date , no study has examined the influence of anxiety on cognition in pd by directly comparing groups of pd patients with and without anxiety while excluding depression . given that research on healthy young adults suggests that anxiety reduces processing capacity and impairs processing efficiency , especially in the central executive and attentional systems of working memory [ 26 , 27 ] , we hypothesized that pd patients with anxiety would show impairments in attentional set - shifting and working memory compared to pd patients without anxiety . furthermore , since previous work , albeit limited , has focused on the influence of symptom laterality on anxiety and cognition , we also explored this relationship . seventeen pd patients with anxiety and thirty - three pd patients without anxiety were included in this study ( see table 1 ) . the cross - sectional data from these participants was taken from a patient database that has been compiled over the past 8 years ( since 2008 ) at the parkinson 's disease research clinic at the brain and mind centre , university of sydney . inclusion criteria involved a diagnosis of idiopathic pd according to the united kingdom parkinson 's disease society brain bank criteria and were confirmed by a neurologist ( sjgl ) . patients also had to have an adequate proficiency in english and have completed a full neuropsychological assessment . ten patients in this study ( 5 pd with anxiety ; 5 pd without anxiety ) were taking psychotropic drugs ( i.e. , benzodiazepine or selective serotonin reuptake inhibitor ) . patients were also excluded if they had other neurological disorders , psychiatric disorders other than affective disorders ( such as anxiety ) , or if they reported a score greater than six on the depression subscale of the hospital anxiety and depression scale ( hads ) . thus , all participants who scored within a depressed ( hads - d > 6 ) range were excluded from this study , in attempt to examine a refined sample of pd patients with and without anxiety in order to determine the independent effect of anxiety on cognition . this research was approved by the human research ethics committee of the university of sydney , and written informed consent was obtained from all participants . self - reported hads was used to assess anxiety in pd and has been previously shown to be a useful measure of clinical anxiety in pd . a cut - off score of > 8 on the anxiety subscale of the hads ( hads - a ) was used to identify pd cases with anxiety ( pda+ ) , while a cut - off score of < 6 on the hads - a was used to identify pd cases without anxiety ( pda ) . this criterion was more stringent than usual ( > 7 cut - off score ) , in effort to create distinct patient groups . the neurological evaluation rated participants according to hoehn and yahr ( h&y ) stages and assessed their motor symptoms using part iii of the revised mds task force unified parkinson 's disease rating scale ( updrs ) . in a similar way this was determined by calculating a total left and right score from rigidity items 3035 , voluntary movement items 3643 , and tremor items 5057 from the mds - updrs part iii ( see table 1 ) . processing speed was assessed using the trail making test , part a ( tmt - a , z - score ) . attentional set - shifting was measured using the trail making test , part b ( tmt - b , z - score ) . working memory was assessed using the digit span forward and backward subtest of the wechsler memory scale - iii ( raw scores ) . language was assessed with semantic and phonemic verbal fluency via the controlled oral word associated test ( cowat animals and letters , z - score ) . the ability to retain learned verbal memory was assessed using the logical memory subtest from the wechsler memory scale - iii ( lm - i z - score , lm - ii z - score , % lm retention z - score ) . the mini - mental state examination ( mmse ) demographic , clinical , and neuropsychological variables were compared between the two groups with the independent t - test or mann whitney u test , depending on whether the variable met parametric assumptions . chi - square tests were used to examine gender and symptom laterality differences between groups . all analyses employed an alpha level of p < 0.05 and were two - tailed . spearman correlations were performed separately in each group to examine associations between anxiety and/or depression ratings and cognitive functions . as expected , the pda+ group reported significant greater levels of anxiety on the hads - a ( u = 0 , p < 0.001 ) and higher total score on the hads ( u = 1 , p < 0.001 ) compared to the pda group ( table 1 ) . groups were matched in age ( t(48 ) = 1.31 , p = 0.20 ) , disease duration ( u = 259 , p = 0.66 ) , updrs - iii score ( u = 250.5 , p = 0.65 ) , h&y ( u = 245 , p = 0.43 ) , ledd ( u = 159.5 , p = 0.80 ) , and depression ( hads - d ) ( u = 190.5 , p = 0.06 ) . additionally , all groups were matched in the distribution of gender ( = 0.098 , p = 0.75 ) and side - affected ( = 0.765 , p = 0.38 ) . there were no group differences for tmt - a performance ( u = 256 , p = 0.62 ) ( table 2 ) ; however , the pda+ group had worse performance on the trail making test part b ( t(46 ) = 2.03 , p = 0.048 ) compared to the pda group ( figure 1 ) . the pda+ group also demonstrated significantly worse performance on the digit span forward subtest ( t(48 ) = 2.22 , p = 0.031 ) and backward subtest ( u = 190.5 , p = 0.016 ) compared to the pda group ( figures 2(a ) and 2(b ) ) . neither semantic verbal fluency ( t(47 ) = 0.70 , p = 0.49 ) nor phonemic verbal fluency ( t(47 ) = 0.39 , p = 0.70 ) differed between groups . logical memory i immediate recall test ( u = 176 , p = 0.059 ) showed a trend that the pda+ group had worse new verbal learning and immediate recall abilities than the pda group . however , logical memory ii test performance ( u = 219 , p = 0.204 ) and logical memory % retention ( u = 242.5 , p = 0.434 ) did not differ between groups . there were also no differences between groups in global cognition ( mmse ) ( u = 222.5 , p = 0.23 ) . participants were split into lpd and rpd , and then further group differences were examined between pda+ and pda. importantly , the groups remained matched in age , disease duration , updrs - iii , dde , h&y stage , and depression but remained significantly different on self - reported anxiety . lpda+ demonstrated worse performance on the digit span forward test ( t(19 ) = 2.29 , p = 0.033 ) compared to lpda , whereas rpda+ demonstrated worse performance on the digit span backward test ( u = 36.5 , p = 0.006 ) , lm - i immediate recall ( u = 37.5 , p = 0.008 ) , and lm - ii ( u = 45.0 , p = 0.021 ) but not lm % retention ( u = 75.5 , p = 0.39 ) compared to rpda. this study is the first to directly compare cognition between pd patients with and without anxiety . the findings confirmed our hypothesis that anxiety negatively influences attentional set - shifting and working memory in pd . more specifically , we found that pd patients with anxiety were more impaired on the trail making test part b which assessed attentional set - shifting , on both digit span tests which assessed working memory and attention , and to a lesser extent on the logical memory test which assessed memory and new verbal learning compared to pd patients without anxiety . taken together , these findings suggest that anxiety in pd may reduce processing capacity and impair processing efficiency , especially in the central executive and attentional systems of working memory in a similar way as seen in young healthy adults [ 26 , 27 ] . although the neurobiology of anxiety in pd remains unknown , many researchers have postulated that anxiety disorders are related to neurochemical changes that occur during the early , premotor stages of pd - related degeneration [ 37 , 38 ] such as nigrostriatal dopamine depletion , as well as cell loss within serotonergic and noradrenergic brainstem nuclei ( i.e. , raphe nuclei and locus coeruleus , resp . , which provide massive inputs to corticolimbic regions ) . over time , chronic dysregulation of adrenocortical and catecholamine functions can lead to hippocampal damage as well as dysfunctional prefrontal neural circuitries [ 39 , 40 ] , which play a key role in memory and attention . recent functional neuroimaging work has suggested that enhanced hippocampal activation during executive functioning and working memory tasks may represent compensatory processes for impaired frontostriatal functions in pd patients compared to controls . therefore , chronic stress from anxiety , for example , may disrupt compensatory processes in pd patients and explain the cognitive impairments specifically in working memory and attention seen in pd patients with anxiety . it has also been suggested that hyperactivation within the putamen may reflect a compensatory striatal mechanism to maintain normal working memory performance in pd patients ; however , losing this compensatory activation has been shown to contribute to poor working memory performance . anxiety in mild pd has been linked to reduced putamen dopamine uptake which becomes more extensive as the disease progresses . this further supports the notion that anxiety may disrupt compensatory striatal mechanisms as well , providing another possible explanation for the cognitive impairments observed in pd patients with anxiety in this study . noradrenergic and serotonergic systems should also be considered when trying to explain the mechanisms by which anxiety may influence cognition in pd . although these neurotransmitter systems are relatively understudied in pd cognition , treating the noradrenergic and serotonergic systems has shown beneficial effects on cognition in pd . selective serotonin reuptake inhibitor , citalopram , was shown to improve response inhibition deficits in pd , while noradrenaline reuptake blocker , atomoxetine , has been recently reported to have promising effects on cognition in pd [ 45 , 46 ] . overall , very few neuroimaging studies have been conducted in pd in order to understand the neural correlates of pd anxiety and its underlying neural pathology . future research should focus on relating anatomical changes and neurochemical changes to neural activation in order to gain a clearer understanding on how these pathologies affect anxiety in pd . to further understand how anxiety and cognitive dysfunction are related , future research should focus on using advanced structural and function imaging techniques to explain both cognitive and neural breakdowns that are associated with anxiety in pd patients . research has indicated that those with amnestic mild cognitive impairment who have more neuropsychiatric symptoms have a greater risk of developing dementia compared to those with fewer neuropsychiatric symptoms . future studies should also examine whether treating neuropsychiatric symptoms might impact the progression of cognitive decline and improve cognitive impairments in pd patients . previous studies have used pd symptom laterality as a window to infer asymmetrical dysfunction of neural circuits . for example , lpd patients have greater inferred right hemisphere pathology , whereas rpd patients have greater inferred left hemisphere pathology . thus , cognitive domains predominantly subserved by the left hemisphere ( e.g. , verbally mediated tasks of executive function and verbal memory ) might be hypothesized to be more affected in rpd than lpd ; however , this remains controversial . it has also been suggested that since anxiety is a common feature of left hemisphere involvement [ 48 , 49 ] , cognitive domains subserved by the left hemisphere may also be more strongly related to anxiety . results from this study showed selective verbal memory deficits in rpd patients with anxiety compared to rpd without anxiety , whereas lpd patients with anxiety had greater attentional / working memory deficits compared to lpd without anxiety . although these results align with previous research , interpretations of these findings should be made with caution due to the small sample size in the lpd comparison specifically . recent work has suggested that the hads questionnaire may underestimate the burden of anxiety related symptomology and therefore be a less sensitive measure of anxiety in pd [ 30 , 50 ] . in addition , our small sample size also limited the statistical power for detecting significant findings . based on these limitations , our findings are likely conservative and underrepresent the true impact anxiety has on cognition in pd . additionally , the current study employed a very brief neuropsychological assessment including one or two tests for each cognitive domain . future studies are encouraged to collect a more complex and comprehensive battery from a larger sample of pd participants in order to better understand the role anxiety plays on cognition in pd . another limitation of this study was the absence of diagnostic interviews to characterize participants ' psychiatric symptoms and specify the type of anxiety disorders included in this study . future studies should perform diagnostic interviews with participants ( e.g. , using dsm - v criteria ) rather than relying on self - reported measures to group participants , in order to better understand whether the type of anxiety disorder ( e.g. , social anxiety , phobias , panic disorders , and generalized anxiety ) influences cognitive performance differently in pd . one advantage the hads questionnaire provided over other anxiety scales was that it assessed both anxiety and depression simultaneously and allowed us to control for coexisting depression . although there was a trend that the pda+ group self - reported higher levels of depression than the pda group , all participants included in the study scored < 6 on the depression subscale of the hads . controlling for depression while assessing anxiety has been identified as a key shortcoming in the majority of recent work . considering many previous studies have investigated the influence of depression on cognition in pd without accounting for the presence of anxiety and the inconsistent findings reported to date , we recommend that future research should try to disentangle the influence of anxiety versus depression on cognitive impairments in pd . considering the growing number of clinical trials for treating depression , there are few if any for the treatment of anxiety in pd . anxiety is a key contributor to decreased quality of life in pd and greatly requires better treatment options . moreover , anxiety has been suggested to play a key role in freezing of gait ( fog ) , which is also related to attentional set - shifting [ 52 , 53 ] . future research should examine the link between anxiety , set - shifting , and fog , in order to determine whether treating anxiety might be a potential therapy for improving fog .""" from transformers import LEDForConditionalGeneration, LEDTokenizer import torch tokenizer = LEDTokenizer.from_pretrained("patrickvonplaten/led-large-16384-pubmed") input_ids = tokenizer(LONG_ARTICLE, return_tensors="pt").input_ids.to("cuda") global_attention_mask = torch.zeros_like(input_ids) # set global_attention_mask on first token global_attention_mask[:, 0] = 1 model = LEDForConditionalGeneration.from_pretrained("patrickvonplaten/led-large-16384-pubmed", return_dict_in_generate=True).to("cuda") sequences = model.generate(input_ids, global_attention_mask=global_attention_mask).sequences summary = tokenizer.batch_decode(sequences) ```

b9eb2ddd57c90ac0b090709d9ce70e68

smilingface88/xlm-roberta-base-finetuned-panx-de-fr

smilingface88

xlm-roberta

transformers

token-classification

mit

['generated_from_trainer']

<!-- 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-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1629 - F1: 0.8584 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2904 | 1.0 | 715 | 0.1823 | 0.8286 | | 0.1446 | 2.0 | 1430 | 0.1626 | 0.8488 | | 0.0941 | 3.0 | 2145 | 0.1629 | 0.8584 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

1d13c5e321ac3b9d217f0c158b868932

anas-awadalla/gpt2-span-head-few-shot-k-64-finetuned-squad-seed-2

anas-awadalla

gpt2

transformers

question-answering

mit

['squad']

['generated_from_trainer']

<!-- 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. --> # gpt2-span-head-few-shot-k-64-finetuned-squad-seed-2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

c6363c7aa5c6aa9168b2f0c9ded8ee45

cross-encoder/mmarco-mMiniLMv2-L12-H384-v1

cross-encoder

xlm-roberta

transformers

text-classification

apache-2.0

['en', 'ar', 'zh', 'nl', 'fr', 'de', 'hi', 'in', 'it', 'ja', 'pt', 'ru', 'es', 'vi', 'multilingual']

['unicamp-dl/mmarco']

[]

# Cross-Encoder for multilingual MS Marco This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages. As a base model, we used the [multilingual MiniLMv2](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco) ## Usage with SentenceTransformers The usage becomes easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]) ``` ## Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ```

71de9fb2ea94513c2354de0ee4e0361d

gaetangate/bart-large_genrl_lcquad2

gaetangate

bart

transformers

text2text-generation

apache-2.0

[]

This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```

97a455d6b0e31ab5a712cdf1a3467ef5

Nurr/wav2vec2-base-finetuned-ks

Nurr

wav2vec2

transformers

audio-classification

apache-2.0

['superb']

['generated_from_trainer']

<!-- 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-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.14.0 - Tokenizers 0.10.3

8e62918f191d95e1edae41ff4c794bf4

rach405/test_trainer3

rach405

bert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- 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_trainer3 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown 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: 5e-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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 1.8785 | 0.396 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Tokenizers 0.11.6

0b571b84f76c1eb1a0e2fe1a3ac15ad7

espnet/kan-bayashi_ljspeech_vits

espnet

espnet

text-to-speech

cc-by-4.0

['en']

['ljspeech']

['espnet', 'audio', 'text-to-speech']

## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_vits` ♻️ Imported from https://zenodo.org/record/5443814/ This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

927cee4f96c308f84bbc9604f2beb1af

NbAiLab/wav2vec2-xlsr-300M-NPSC-LM

NbAiLab

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['nb-NO']

['NbAiLab/NPSC']

['automatic-speech-recognition', 'generated_from_trainer', False, 'nb-NO', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard']

# XLS-R-300M-LM - Norwegian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Norwegian [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) dataset. ### Scores without Language Model Without using a language model, it achieves the following scores on the NPSC Eval set It achieves the following results on the evaluation set without a language model: - WER: 0.2110 - CER: 0.0622 ### Scores with Language Model A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values. - WER: 0.1540 - CER: 0.0548 ## Team The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order. ## Model description This current version is based on checkpoint 8500 of [NbAiLab/wav2vec2-xlsr-300M-NPSC-OH](https://huggingface.co/NbAiLab/wav2vec2-xlsr-300M-NPSC-OH). ## Intended uses & limitations Demo version only. The model will be updated later this week. ## Training and evaluation data The model is trained and evaluated on [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC). Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC.. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 2000 - num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs) - mixed_precision_training: Native AMP

55e8c3ac00b0e18cd938744f6ba49e2a

kadirnar/AnimeSR_v2

kadirnar

object-detection

apache-2.0

['object-detection', 'computer-vision', 'gan', 'animegan']

### Model Description [AnimeSR](https://arxiv.org/abs/2206.07038): Learning Real-World Super-Resolution Models for Animation Videos ### Installation ``` pip install animesr ``` ### Anime GAN ```python from animesr.inference_animesr_video import main main(source='test.mp4', 'kadirnar/AnimeSR_v2') ``` ### BibTeX Entry and Citation Info ``` @InProceedings{wu2022animesr, author={Wu, Yanze and Wang, Xintao and Li, Gen and Shan, Ying}, title={AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos}, booktitle={Advances in Neural Information Processing Systems}, year={2022} } ```

d080e25f2125d2b85cb5b996714004ab

lighteternal/SSE-TUC-mt-el-en-cased

lighteternal

fsmt

transformers

translation

apache-2.0

['en', 'el']

['translation']

## Greek to English NMT ## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC) * source languages: el * target languages: en * licence: apache-2.0 * dataset: Opus, CCmatrix * model: transformer(fairseq) * pre-processing: tokenization + BPE segmentation * metrics: bleu, chrf ### Model description Trained using the Fairseq framework, transformer_iwslt_de_en architecture.\\ BPE segmentation (20k codes).\\ Mixed-case model. ### How to use ``` from transformers import FSMTTokenizer, FSMTForConditionalGeneration mname = "lighteternal/SSE-TUC-mt-el-en-cased" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) text = "Ο όρος τεχνητή νοημοσύνη αναφέρεται στον κλάδο της πληροφορικής ο οποίος ασχολείται με τη σχεδίαση και την υλοποίηση υπολογιστικών συστημάτων που μιμούνται στοιχεία της ανθρώπινης συμπεριφοράς ." encoded = tokenizer.encode(text, return_tensors='pt') outputs = model.generate(encoded, num_beams=5, num_return_sequences=5, early_stopping=True) for i, output in enumerate(outputs): i += 1 print(f"{i}: {output.tolist()}") decoded = tokenizer.decode(output, skip_special_tokens=True) print(f"{i}: {decoded}") ``` ## Training data Consolidated corpus from Opus and CC-Matrix (~6.6GB in total) ## Eval results Results on Tatoeba testset (EL-EN): | BLEU | chrF | | ------ | ------ | | 79.3 | 0.795 | Results on XNLI parallel (EL-EN): | BLEU | chrF | | ------ | ------ | | 66.2 | 0.623 | ### BibTeX entry and citation info Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW ### Acknowledgement The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)

28b80a448d069bdea6e559043be5cdee

Helsinki-NLP/opus-mt-es-es

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-es-es * source languages: es * target languages: es * OPUS readme: [es-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-es/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-es/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-es/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.es.es | 51.7 | 0.688 |

1d136fe532dd280bd6e00cc162b8fca1

valhalla/distilt5-qa-qg-hl-6-4

valhalla

t5

transformers

text2text-generation

mit

['squad']

['question-generation', 'distilt5', 'distilt5-qg']

## DistilT5 for question-generation This is distilled version of [t5-small-qa-qg-hl](https://huggingface.co/valhalla/t5-small-qa-qg-hl) model trained for question answering and answer aware question generation tasks. The model is distilled using the **No Teacher Distillation** method proposed by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart). We just copy alternating layers from `t5-small-qa-qg-hl` and finetune more on the same data. Following table lists other distilled models and their metrics. | Name | BLEU-4 | METEOR | ROUGE-L | QA-EM | QA-F1 | |---------------------------------------------------------------------------------|---------|---------|---------|--------|--------| | [distilt5-qg-hl-6-4](https://huggingface.co/valhalla/distilt5-qg-hl-6-4) | 18.4141 | 24.8417 | 40.3435 | - | - | | [distilt5-qa-qg-hl-6-4](https://huggingface.co/valhalla/distilt5-qa-qg-hl-6-4) | 18.6493 | 24.9685 | 40.5605 | 76.13 | 84.659 | | [distilt5-qg-hl-12-6](https://huggingface.co/valhalla/distilt5-qg-hl-12-6) | 20.5275 | 26.5010 | 43.2676 | - | - | | [distilt5-qa-qg-hl-12-6](https://huggingface.co/valhalla/distilt5-qa-qg-hl-12-6)| 20.6109 | 26.4533 | 43.0895 | 81.61 | 89.831 | You can play with the model using the inference API. Here's how you can use it For QG `generate question: <hl> 42 <hl> is the answer to life, the universe and everything.` For QA `question: What is 42 context: 42 is the answer to life, the universe and everything.` For more deatils see [this](https://github.com/patil-suraj/question_generation) repo. ### Model in action 🚀 You'll need to clone the [repo](https://github.com/patil-suraj/question_generation). [](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb) ```python3 from pipelines import pipeline nlp = pipeline("multitask-qa-qg", model="valhalla/distilt5-qa-qg-hl-6-4") # to generate questions simply pass the text nlp("42 is the answer to life, the universe and everything.") => [{'answer': '42', 'question': 'What is the answer to life?'}] # for qa pass a dict with "question" and "context" nlp({ "question": "What is 42 ?", "context": "42 is the answer to life, the universe and everything." }) => 'the answer to life, the universe and everything' ```

32965bddcd33dfcff0c34a53916e7f6d

Helsinki-NLP/opus-mt-de-eo

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-de-eo * source languages: de * target languages: eo * OPUS readme: [de-eo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-eo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-eo/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-eo/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-eo/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.de.eo | 48.6 | 0.673 |

88beedd800abeeaaedb7d965a9a4712a

TehranNLP-org/bert-large-hateXplain

TehranNLP-org

bert

transformers

text-classification

apache-2.0

['en']

['generated_from_trainer']

<!-- 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. --> # SEED0042 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the HATEXPLAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.7731 - Accuracy: 0.4079 - Accuracy 0: 0.8027 - Accuracy 1: 0.1869 - Accuracy 2: 0.2956 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: not_parallel - gradient_accumulation_steps: 32 - 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: 150 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy 0 | Accuracy 1 | Accuracy 2 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:----------:|:----------:| | No log | 1.0 | 480 | 0.8029 | 0.4235 | 0.7589 | 0.0461 | 0.5985 | | No log | 2.0 | 960 | 0.7574 | 0.4011 | 0.7470 | 0.1831 | 0.3376 | | No log | 3.0 | 1440 | 0.7731 | 0.4079 | 0.8027 | 0.1869 | 0.2956 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 2.1.0 - Tokenizers 0.11.6

f10b188446ffe9408631cdcae8403c7f

naveensb8182/vit-base-beans

naveensb8182

vit

transformers

image-classification

apache-2.0

['beans']

['generated_from_trainer']

<!-- 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. --> # vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9774 - Loss: 0.0876 ## 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: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.26 | 1.0 | 130 | 0.9549 | 0.2285 | | 0.277 | 2.0 | 260 | 0.9925 | 0.1066 | | 0.1629 | 3.0 | 390 | 0.9699 | 0.1069 | | 0.0963 | 4.0 | 520 | 0.9774 | 0.0885 | | 0.1569 | 5.0 | 650 | 0.9774 | 0.0876 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cpu - Datasets 2.8.0 - Tokenizers 0.13.2

9d404808c7fd6f769b5346e81c0559b3

wavymulder/modelshoot

wavymulder

diffusers

text-to-image

creativeml-openrail-m

['en']

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers']

**Modelshoot Style**  [*CKPT DOWNLOAD LINK*](https://huggingface.co/wavymulder/modelshoot/resolve/main/modelshoot-1.0.ckpt) Use `modelshoot style` in your prompt (I recommend at the start) I also suggest your prompts include subject and location, for example "`amy adams at the construction site`" , as this helps the model to resolve backgrounds and small details. Modelshoot is a Dreambooth model trained from 1.5 with VAE on a diverse set of photographs of people. The goal was to create a model focused on full to medium body shots, with an emphasis on cool clothing and a fashion-shoot aesthetic. A result of the composition is that when your subject is further away, their face will usually look worse (and for celebrities, less like them). This limitation of training on 512x512 can be fixed with inpainting, and I plan on revisiting this model at higher resolution in the future. Modelshoot style works best when using a tall aspect ratio. This model was inspired by all the great responses to Analog Diffusion, especially ones where you all trained yourselves in and created awesome, fashionable photos! I hope that this model allows even greater images :) Please see [this document where I share the parameters (prompt, sampler, seed, etc.) used for all example images above.](https://huggingface.co/wavymulder/modelshoot/resolve/main/parameters_for_samples.txt) See below a batch example and how the model helps ensure a fashion-shoot composition without any excessive prompting. No face restoration used for any examples on this page, for demonstration purposes. 

fcdefe3fcb9bf06f5491143c017b8b62

saattrupdan/nbailab-base-ner-scandi

saattrupdan

bert

transformers

token-classification

mit

['da', False, 'nb', 'nn', 'sv', 'fo', 'is']

['dane', 'norne', 'wikiann', 'suc3.0']

[]

# ScandiNER - Named Entity Recognition model for Scandinavian Languages This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) for Named Entity Recognition for Danish, Norwegian (both Bokmål and Nynorsk), Swedish, Icelandic and Faroese. It has been fine-tuned on the concatenation of [DaNE](https://aclanthology.org/2020.lrec-1.565/), [NorNE](https://arxiv.org/abs/1911.12146), [SUC 3.0](https://spraakbanken.gu.se/en/resources/suc3) and the Icelandic and Faroese parts of the [WikiANN](https://aclanthology.org/P17-1178/) dataset. It also works reasonably well on English sentences, given the fact that the pretrained model is also trained on English data along with Scandinavian languages. The model will predict the following four entities: | **Tag** | **Name** | **Description** | | :------ | :------- | :-------------- | | `PER` | Person | The name of a person (e.g., *Birgitte* and *Mohammed*) | | `LOC` | Location | The name of a location (e.g., *Tyskland* and *Djurgården*) | | `ORG` | Organisation | The name of an organisation (e.g., *Bunnpris* and *Landsbankinn*) | | `MISC` | Miscellaneous | A named entity of a different kind (e.g., *Ūjķnustu pund* and *Mona Lisa*) | ## Quick start You can use this model in your scripts as follows: ```python >>> from transformers import pipeline >>> import pandas as pd >>> ner = pipeline(task='ner', ... model='saattrupdan/nbailab-base-ner-scandi', ... aggregation_strategy='first') >>> result = ner('Borghild kjøper seg inn i Bunnpris') >>> pd.DataFrame.from_records(result) entity_group score word start end 0 PER 0.981257 Borghild 0 8 1 ORG 0.974099 Bunnpris 26 34 ``` ## Performance The following is the Micro-F1 NER performance on Scandinavian NER test datasets, compared with the current state-of-the-art. The models have been evaluated on the test set along with 9 bootstrapped versions of it, with the mean and 95% confidence interval shown here: | **Model ID** | **DaNE** | **NorNE-NB** | **NorNE-NN** | **SUC 3.0** | **WikiANN-IS** | **WikiANN-FO** | **Average** | | :----------- | -------: | -----------: | -----------: | ----------: | -------------: | -------------: | ----------: | | saattrupdan/nbailab-base-ner-scandi | **87.44 ± 0.81** | **91.06 ± 0.26** | **90.42 ± 0.61** | **88.37 ± 0.17** | **88.61 ± 0.41** | **90.22 ± 0.46** | **89.08 ± 0.46** | | chcaa/da\_dacy\_large\_trf | 83.61 ± 1.18 | 78.90 ± 0.49 | 72.62 ± 0.58 | 53.35 ± 0.17 | 50.57 ± 0.46 | 51.72 ± 0.52 | 63.00 ± 0.57 | | RecordedFuture/Swedish-NER | 64.09 ± 0.97 | 61.74 ± 0.50 | 56.67 ± 0.79 | 66.60 ± 0.27 | 34.54 ± 0.73 | 42.16 ± 0.83 | 53.32 ± 0.69 | | Maltehb/danish-bert-botxo-ner-dane | 69.25 ± 1.17 | 60.57 ± 0.27 | 35.60 ± 1.19 | 38.37 ± 0.26 | 21.00 ± 0.57 | 27.88 ± 0.48 | 40.92 ± 0.64 | | Maltehb/-l-ctra-danish-electra-small-uncased-ner-dane | 70.41 ± 1.19 | 48.76 ± 0.70 | 27.58 ± 0.61 | 35.39 ± 0.38 | 26.22 ± 0.52 | 28.30 ± 0.29 | 39.70 ± 0.61 | | radbrt/nb\_nocy\_trf | 56.82 ± 1.63 | 68.20 ± 0.75 | 69.22 ± 1.04 | 31.63 ± 0.29 | 20.32 ± 0.45 | 12.91 ± 0.50 | 38.08 ± 0.75 | Aside from its high accuracy, it's also substantially **smaller** and **faster** than the previous state-of-the-art: | **Model ID** | **Samples/second** | **Model size** | | :----------- | -----------------: | -------------: | | saattrupdan/nbailab-base-ner-scandi | 4.16 ± 0.18 | 676 MB | | chcaa/da\_dacy\_large\_trf | 0.65 ± 0.01 | 2,090 MB | ## 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 - gradient_accumulation_steps: 4 - 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: 90135.90000000001 - num_epochs: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Micro F1 | Micro F1 No Misc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------------:| | 0.6682 | 1.0 | 2816 | 0.0872 | 0.6916 | 0.7306 | | 0.0684 | 2.0 | 5632 | 0.0464 | 0.8167 | 0.8538 | | 0.0444 | 3.0 | 8448 | 0.0367 | 0.8485 | 0.8783 | | 0.0349 | 4.0 | 11264 | 0.0316 | 0.8684 | 0.8920 | | 0.0282 | 5.0 | 14080 | 0.0290 | 0.8820 | 0.9033 | | 0.0231 | 6.0 | 16896 | 0.0283 | 0.8854 | 0.9060 | | 0.0189 | 7.0 | 19712 | 0.0253 | 0.8964 | 0.9156 | | 0.0155 | 8.0 | 22528 | 0.0260 | 0.9016 | 0.9201 | | 0.0123 | 9.0 | 25344 | 0.0266 | 0.9059 | 0.9233 | | 0.0098 | 10.0 | 28160 | 0.0280 | 0.9091 | 0.9279 | | 0.008 | 11.0 | 30976 | 0.0309 | 0.9093 | 0.9287 | | 0.0065 | 12.0 | 33792 | 0.0313 | 0.9103 | 0.9284 | | 0.0053 | 13.0 | 36608 | 0.0322 | 0.9078 | 0.9257 | | 0.0046 | 14.0 | 39424 | 0.0343 | 0.9075 | 0.9256 | ### Framework versions - Transformers 4.10.3 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

eaafa570f4ee95508ae391d0c4b18e50

Lucetepolis/OctaFuzz

Lucetepolis

diffusers

text-to-image

creativeml-openrail-m

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers']

# HXDC Counterfeit-V2.5 - <a href="https://huggingface.co/gsdf/Counterfeit-V2.5">Download</a><br/> Treebark - <a href="https://huggingface.co/HIZ/aichan_pick">Download</a><br/> HyperBomb, FaceBomb - <a href="https://huggingface.co/mocker/KaBoom">Download</a><br/> qwerty - <a href="https://huggingface.co/1q2W3e/qwerty">Download</a><br/> ultracolor.v4 - <a href="https://huggingface.co/xdive/ultracolor.v4">Download</a><br/> donko-mix-hard - <a href="https://civitai.com/models/7037/donko-mix-nsfw-hard">Download</a><br/> OrangePastelV2 - ~~Download~~ Currently not available.<br/> smix 1.12121 - <a href="https://civitai.com/models/8019/smix-1-series">Download</a><br/> viewer-mix - <a href="https://civitai.com/models/7813/viewer-mix">Download</a><br/> 0012-half - <a href="https://huggingface.co/1q2W3e/Attached-model_collection">Download</a><br/> Null v2.2 - <a href="https://civitai.com/models/8173/null-v22">Download</a><br/> school anime - <a href="https://civitai.com/models/7189/school-anime">Download</a><br/> tlqkfniji7 - <a href="https://huggingface.co/uiouiouio/The_lovely_quality_kahlua_flavour">Download</a><br/> 7th_anime_v3_B - <a href="https://huggingface.co/syaimu/7th_Layer">Download</a><br/> Crowbox-Vol.1 - <a href="https://huggingface.co/kf1022/Crowbox-Vol.1">Download</a><br/> EasyNegative and pastelmix-lora seem to work well with the models. EasyNegative - <a href="https://huggingface.co/datasets/gsdf/EasyNegative">Download</a><br/> pastelmix-lora - <a href="https://huggingface.co/andite/pastel-mix">Download</a> # Formula ``` Counterfeit-V2.5 + Treebark = ct base_alpha = 0.009901 Weight values = 0.259221, 0.094699, 0.186355, 0.344377, 0.54691, 0.535689, 0.526122, 0.420305, 0.312004, 0.40172, 0.452608, 0.481439, 0.029126, 0.492655, 0.478894, 0.443794, 0.284518, 0.24424, 0.284451, 0.382469, 0.282082, 0.18387, 0.126064, 0.113941, 0.103878 ct + HyperBomb = cth base_alpha = 0.09009 Weight values = 0.208912, 0.290962, 0.44034, 0.426141, 0.294959, 0.258193, 0.279347, 0.219226, 0.100589, 0.076065, 0.061552, 0.053125, 0.225564, 0.013679, 0.029582, 0.067917, 0.209599, 0.238881, 0.209736, 0.097528, 0.143293, 0.18856, 0.227611, 0.336235, 0.40562 cth + qwerty = cthq base_alpha = 0.008929 Weight values = 0.298931, 0.286255, 0.185812, 0.136147, 0.100038, 0.09741, 0.069466, 0.065465, 0.099956, 0.218813, 0.27544, 0.304705, 0.184049, 0.021782, 0.051109, 0.115061, 0.291535, 0.319518, 0.291441, 0.197459, 0.295056, 0.359111, 0.375537, 0.264379, 0.170006 cthq + ultracolor.v4 = cthqu base_alpha = 0.081967 Weight values = 0.044348, 0.051224, 0.092643, 0.0896, 0.047055, 0.03864, 0.032217, 0.034381, 0.032329, 0.017, 0.009525, 0.005618, 0.380228, 0.060561, 0.083015, 0.128444, 0.233262, 0.247876, 0.234218, 0.103302, 0.082694, 0.111921, 0.235504, 0.634374, 0.746614 cthqu + FaceBomb = cthquf base_alpha = 0.45045 Weight values = 0.304652, 0.108189, 0.113682, 0.116402, 0.118828, 0.11284, 0.095841, 0.065612, 0.035945, 0.033428, 0.032195, 0.03155, 0.03663, 0.006005, 0.008193, 0.012592, 0.022593, 0.023941, 0.02257, 0.019395, 0.027618, 0.032024, 0.029911, 0.015144, 0.010908 cthquf + donko-mix-hard = cthqufd base_alpha = 0.310559 Weight values = 0.041071, 0.033818, 0.035788, 0.036933, 0.038236, 0.037834, 0.040386, 0.045727, 0.049152, 0.025509, 0.0135, 0.007091, 0.035336, 0.009262, 0.016837, 0.031714, 0.063923, 0.068124, 0.063941, 0.051919, 0.076044, 0.091518, 0.094579, 0.081523, 0.077707 cthqufd + OrangePastelV2 = OctaFuzz base_alpha = 0.03012 Weight values = 0.045454, 0.044635, 0.071192, 0.078145, 0.074833, 0.072486, 0.069609, 0.08331, 0.082494, 0.043373, 0.022197, 0.010507, 0.03413, 0.009176, 0.016555, 0.030733, 0.06007, 0.063741, 0.059989, 0.049022, 0.069114, 0.078421, 0.07162, 0.029375, 0.016293 smix 1.12121 + viewer-mix = sv base_alpha = 0.230769 Weight values = 0.395271, 0.35297, 0.359395, 0.382984, 0.448508, 0.468333, 0.478042, 0.475167, 0.419157, 0.446681, 0.469808, 0.48688, 0.230769, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5 sv + 0012-half = sv0 base_alpha = 0.434783 Weight values = 0.096641, 0.097719, 0.100011, 0.105301, 0.118931, 0.122252, 0.120899, 0.11391, 0.15397, 0.407393, 0.526559, 0.587752, 0.071429, 0.326817, 0.315594, 0.291682, 0.229445, 0.220024, 0.229364, 0.30164, 0.31157, 0.309196, 0.281226, 0.145209, 0.089865 sv0 + Null v2.2 = sv0n base_alpha = 0.115385 Weight values = 0.132862, 0.1371, 0.108727, 0.104247, 0.117468, 0.122796, 0.131157, 0.14836, 0.213205, 0.184383, 0.170088, 0.16255, 0.176471, 0.013049, 0.029363, 0.062385, 0.138653, 0.149139, 0.138776, 0.119286, 0.183455, 0.228237, 0.255516, 0.296091, 0.311362 sv0n + school anime = sv0ns base_alpha = 0.103448 Weight values = 0.087455, 0.088646, 0.114848, 0.110151, 0.070954, 0.064852, 0.054146, 0.06643, 0.083591, 0.111871, 0.125259, 0.132157, 0.055556, 0.014513, 0.032747, 0.067662, 0.139412, 0.148332, 0.139177, 0.054834, 0.040531, 0.031203, 0.02771, 0.029855, 0.03066 sv0ns + tlqkfniji7 = sv0nst base_alpha = 0.25641 Weight values = 0.366264, 0.082457, 0.061703, 0.0743, 0.128699, 0.132356, 0.090334, 0.073644, 0.120288, 0.066093, 0.038035, 0.022911, 0.016393, 0.010271, 0.010979, 0.012331, 0.015099, 0.015235, 0.014313, 0.006851, 0.005245, 0.005269, 0.008194, 0.021708, 0.026685 sv0nst + 7th_anime_v3_B = sv0nst7 base_alpha = 0.025 Weight values = 0.270768, 0.082819, 0.089464, 0.099695, 0.122101, 0.11876, 0.079592, 0.057662, 0.096981, 0.056373, 0.033881, 0.021306, 0.016129, 0.004163, 0.005616, 0.008379, 0.013987, 0.01468, 0.013977, 0.00666, 0.004674, 0.003356, 0.002823, 0.002944, 0.002989 sv0nst7 + Crowbox-Vol.1 = OctaBlend base_alpha = 0.007444 Weight values = 0.036592, 0.028764, 0.033246, 0.051828, 0.096045, 0.099435, 0.054162, 0.020355, 0.01281, 0.027376, 0.035261, 0.039613, 0.005348, 0.029654, 0.026405, 0.020164, 0.00725, 0.005724, 0.007621, 0.016328, 0.014867, 0.025298, 0.058555, 0.172774, 0.208144 OctaFuzz + OctaBlend = HXDC base_alpha = 0.5 Weight values = 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5 ``` # Converted weights    # Samples All of the images use following negatives/settings. EXIF preserved. ``` Negative prompt: (worst quality, low quality:1.4), EasyNegative, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits Steps: 28, Sampler: DPM++ 2M Karras, CFG scale: 7, Size: 768x512, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 1.5, Hires steps: 14, Hires upscaler: Latent (nearest-exact) ``` # OctaFuzz         # OctaBlend         # HXDC        

2d075ca48952f86a986cbd4dde650cb1

KoichiYasuoka/roberta-base-chinese-upos

KoichiYasuoka

roberta

transformers

token-classification

cc-by-sa-4.0

['zh']

['universal_dependencies']

['chinese', 'token-classification', 'pos', 'dependency-parsing']

# roberta-base-chinese-upos ## Model Description This is a RoBERTa model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [roberta-base-chinese](https://huggingface.co/KoichiYasuoka/roberta-base-chinese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). ## How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-chinese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-chinese-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/roberta-base-chinese-upos") ``` ## See Also [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models

11d01e1d7810de17cd461162a1663690

sinchir0/my_awesome_wnut_model

sinchir0

distilbert

transformers

token-classification

apache-2.0

['wnut_17']

['generated_from_trainer']

<!-- 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. --> # my_awesome_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2808 - Precision: 0.5314 - Recall: 0.2901 - F1: 0.3753 - Accuracy: 0.9404 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2960 | 0.3920 | 0.1900 | 0.2559 | 0.9352 | | No log | 2.0 | 426 | 0.2808 | 0.5314 | 0.2901 | 0.3753 | 0.9404 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

4092a14a44d646d65fe985591ea38aaa

x67/shortjourney

x67

diffusers

text-to-image

creativeml-openrail-m

['en']

['stable-diffusion', 'text-to-image']

# Shortjourney is a Stable Diffusion model that lets you generate Midjourney style images with simple prompts This model was finetuned over the [22h/vintedois-diffusion](https://huggingface.co/22h/vintedois-diffusion-v0-1) (SD 1.5) model with some Midjourney style images. This allows it to create stunning images without long and tedious prompt engineering. Trigger Phrase: "**sjrny-v1 style**" e.g. "sjrny-v1 style paddington bear" **You can use this model for personal or commercial business. I am not liable for it's use/mis-use... you are!** The model does portraits extremely well. For landscapes, try using 512x832 or some other landscape aspect ratio. ### Examples * Prompt: sjrny-v1 style portrait of a woman, cosmic * CFG scale: 7 * Scheduler: Euler_a * Steps: 30 * Dimensions: 512x512 * Seed: 557913691  * Prompt: sjrny-v1 style paddington bear * CFG scale: 7 * Scheduler: Euler_a * Steps: 30 * Dimensions: 512x512  * Prompt: sjrny-v1 style livingroom, cinematic lighting, 4k, unreal engine * CFG scale: 7 * Scheduler: Euler_a * Steps: 30 * Dimensions: 512x832 * Seed: 638363858  * Prompt: sjrny-v1 style dream landscape, cosmic * CFG scale: 7 * Scheduler: Euler_a * Steps: 30 * Dimensions: 512x832  ### Disclaimer I am your typical person who is just curious about these diffusion models. I apologize in advance for any issue with the model. I am learning. Pull Requests are much appreciated!

6c44e1bd800cef537edd02b82e6bcb28

Satyamatury/satya-matury-asr-task-2-hindidata

Satyamatury

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice']

['generated_from_trainer']

<!-- 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. --> # satya-matury-asr-task-2-hindidata This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.0972 - Wer: 0.9942 ## 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.6515 | 44.42 | 400 | 3.0972 | 0.9942 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

e40c02fc963d018ded9f973330df4c83

ImageIN/resnet-50_finetuned

ImageIN

resnet

transformers

image-classification

apache-2.0

['generated_from_trainer']

<!-- 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. --> # resnet-50_finetuned This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7209 - Precision: 0.3702 - Recall: 0.5 - F1: 0.4254 - Accuracy: 0.7404 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 46 | 0.6599 | 0.3702 | 0.5 | 0.4254 | 0.7404 | | No log | 2.0 | 92 | 0.6725 | 0.3702 | 0.5 | 0.4254 | 0.7404 | | No log | 3.0 | 138 | nan | 0.8714 | 0.5062 | 0.4384 | 0.7436 | | No log | 4.0 | 184 | nan | 0.8714 | 0.5062 | 0.4384 | 0.7436 | | No log | 5.0 | 230 | nan | 0.8714 | 0.5062 | 0.4384 | 0.7436 | | No log | 6.0 | 276 | nan | 0.8714 | 0.5062 | 0.4384 | 0.7436 | | No log | 7.0 | 322 | nan | 0.8714 | 0.5062 | 0.4384 | 0.7436 | | No log | 8.0 | 368 | nan | 0.8714 | 0.5062 | 0.4384 | 0.7436 | | No log | 9.0 | 414 | nan | 0.8714 | 0.5062 | 0.4384 | 0.7436 | | No log | 10.0 | 460 | 0.7209 | 0.3702 | 0.5 | 0.4254 | 0.7404 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

11b6f594ba5950db0caccd7b7fa33d64

edangx100/t5-small-finetuned-wikisql

edangx100

t5

transformers

text2text-generation

apache-2.0

['wiki_sql']

['generated_from_trainer']

<!-- 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. --> # t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wiki_sql dataset. It achieves the following results on the evaluation set: - Loss: 0.1246 - Rouge2 Precision: 0.8187 - Rouge2 Recall: 0.7269 - Rouge2 Fmeasure: 0.7629 ## 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: 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.1952 | 1.0 | 4049 | 0.1567 | 0.7948 | 0.7057 | 0.7406 | | 0.167 | 2.0 | 8098 | 0.1382 | 0.8092 | 0.7171 | 0.7534 | | 0.1517 | 3.0 | 12147 | 0.1296 | 0.8145 | 0.7228 | 0.7589 | | 0.1433 | 4.0 | 16196 | 0.1260 | 0.8175 | 0.7254 | 0.7617 | | 0.1414 | 5.0 | 20245 | 0.1246 | 0.8187 | 0.7269 | 0.7629 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

1d598f5f0e0893a01d0cc79d74f6de82

muhtasham/small-mlm-glue-rte-target-glue-wnli

muhtasham

bert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- 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. --> # small-mlm-glue-rte-target-glue-wnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.6097 - Accuracy: 0.0704 ## 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: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.628 | 25.0 | 500 | 2.6799 | 0.1127 | | 0.2976 | 50.0 | 1000 | 5.1702 | 0.0845 | | 0.1272 | 75.0 | 1500 | 6.3920 | 0.0845 | | 0.0709 | 100.0 | 2000 | 7.6097 | 0.0704 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

63f44b34a509beb039398e33ece26b1f

egumasa/roberta-base-finetuned-academic

egumasa

roberta

transformers

fill-mask

mit

['elsevier-oa-cc-by']

['generated_from_trainer']

<!-- 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. --> # roberta-base-finetuned-academic This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the elsevier-oa-cc-by dataset. It achieves the following results on the evaluation set: - Loss: 2.1158 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.1903 | 0.25 | 1025 | 2.0998 | | 2.1752 | 0.5 | 2050 | 2.1186 | | 2.1864 | 0.75 | 3075 | 2.1073 | | 2.1874 | 1.0 | 4100 | 2.1177 | | 2.1669 | 1.25 | 5125 | 2.1091 | | 2.1859 | 1.5 | 6150 | 2.1212 | | 2.1783 | 1.75 | 7175 | 2.1096 | | 2.1734 | 2.0 | 8200 | 2.0998 | | 2.1712 | 2.25 | 9225 | 2.0972 | | 2.1812 | 2.5 | 10250 | 2.1051 | | 2.1811 | 2.75 | 11275 | 2.1150 | | 2.1826 | 3.0 | 12300 | 2.1097 | | 2.172 | 3.25 | 13325 | 2.1115 | | 2.1745 | 3.5 | 14350 | 2.1098 | | 2.1758 | 3.75 | 15375 | 2.1101 | | 2.1834 | 4.0 | 16400 | 2.1232 | | 2.1836 | 4.25 | 17425 | 2.1052 | | 2.1791 | 4.5 | 18450 | 2.1186 | | 2.172 | 4.75 | 19475 | 2.1039 | | 2.1797 | 5.0 | 20500 | 2.1015 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

330dd76dd09faef226445c57bfa733a1

asimokby/fakeBert

asimokby

text-classification

mit

['text-classification', 'PyTorch', 'Transformers']

# fakeBert This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a [news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) from Kaggle. ## Model description Fine-tuning Bert for text classification. ## Training and evaluation data Training & Validation: [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) Testing: [Fake News Detection Challenge KDD 2020](https://www.kaggle.com/competitions/fakenewskdd2020/overview) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-5 - train_batch_size: 16 - eval_batch_size: 16 - optimizer: AdamW

3787d2fcf2fc4954436e369b45baa8b6

dnciic/dnciic

dnciic

text-to-image

creativeml-openrail-m

['text-to-image', 'stable-diffusion']

### dnciic Dreambooth model trained by dnciic with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

7d179bd2e0280cfee1b80947e3d5ff90

gokuls/distilbert_add_GLUE_Experiment_sst2_96

gokuls

distilbert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- 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_add_GLUE_Experiment_sst2_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5220 - Accuracy: 0.7683 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6871 | 1.0 | 264 | 0.6971 | 0.5092 | | 0.6867 | 2.0 | 528 | 0.6968 | 0.5092 | | 0.6865 | 3.0 | 792 | 0.6978 | 0.5092 | | 0.6864 | 4.0 | 1056 | 0.6951 | 0.5092 | | 0.514 | 5.0 | 1320 | 0.5220 | 0.7683 | | 0.3536 | 6.0 | 1584 | 0.5425 | 0.7764 | | 0.297 | 7.0 | 1848 | 0.5412 | 0.7901 | | 0.2677 | 8.0 | 2112 | 0.5740 | 0.7729 | | 0.245 | 9.0 | 2376 | 0.5970 | 0.7741 | | 0.2313 | 10.0 | 2640 | 0.6024 | 0.7856 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2

78f162a67c7402d21ce61ce6cc8c5b7d

asp2131/COVER_TUNE

asp2131

diffusers

text-to-image

creativeml-openrail-m

['text-to-image', 'stable-diffusion']

### {INSTANCE_NAME} Dreambooth model trained by asp2131 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept: 

057e630a950b659a89f40b706c066d96

hackertec/roberta-base-bne-finetuned-amazon_reviews_multi

hackertec

roberta

transformers

text-classification

cc-by-4.0

['amazon_reviews_multi']

['generated_from_trainer']

<!-- 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. --> # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2557 - Accuracy: 0.9085 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2296 | 1.0 | 125 | 0.2557 | 0.9085 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

670ca786193d39994f51f66886ef2020

obss/mt5-small-3task-both-tquad2

obss

mt5

transformers

text2text-generation

cc-by-4.0

['tr']

['tquad1', 'tquad2', 'xquad']

['text2text-generation', 'question-generation', 'answer-extraction', 'question-answering', 'text-generation']

# mt5-small for Turkish Question Generation Automated question generation and question answering using text-to-text transformers by OBSS AI. ```python from core.api import GenerationAPI generation_api = GenerationAPI('mt5-small-3task-both-tquad2', qg_format='both') ``` ## Citation 📜 ``` @article{akyon2022questgen, author = {Akyon, Fatih Cagatay and Cavusoglu, Ali Devrim Ekin and Cengiz, Cemil and Altinuc, Sinan Onur and Temizel, Alptekin}, doi = {10.3906/elk-1300-0632.3914}, journal = {Turkish Journal of Electrical Engineering and Computer Sciences}, title = {{Automated question generation and question answering from Turkish texts}}, url = {https://journals.tubitak.gov.tr/elektrik/vol30/iss5/17/}, year = {2022} } ``` ## Overview ✔️ **Language model:** mt5-small **Language:** Turkish **Downstream-task:** Extractive QA/QG, Answer Extraction **Training data:** TQuADv2-train **Code:** https://github.com/obss/turkish-question-generation **Paper:** https://journals.tubitak.gov.tr/elektrik/vol30/iss5/17/ ## Hyperparameters ``` batch_size = 256 n_epochs = 15 base_LM_model = "mt5-small" max_source_length = 512 max_target_length = 64 learning_rate = 1.0e-3 task_lisst = ["qa", "qg", "ans_ext"] qg_format = "both" ``` ## Performance Refer to [paper](https://arxiv.org/abs/2111.06476). ## Usage 🔥 ```python from core.api import GenerationAPI generation_api = GenerationAPI('mt5-small-3task-both-tquad2', qg_format='both') context = """ Bu modelin eğitiminde, Türkçe soru cevap verileri kullanılmıştır. Çalışmada sunulan yöntemle, Türkçe metinlerden otomatik olarak soru ve cevap üretilebilir. Bu proje ile paylaşılan kaynak kodu ile Türkçe Soru Üretme / Soru Cevaplama konularında yeni akademik çalışmalar yapılabilir. Projenin detaylarına paylaşılan Github ve Arxiv linklerinden ulaşılabilir. """ # a) Fully Automated Question Generation generation_api(task='question-generation', context=context) # b) Question Answering question = "Bu model ne işe yarar?" generation_api(task='question-answering', context=context, question=question) # b) Answer Extraction generation_api(task='answer-extraction', context=context) ```

499cc1fc10410b730aba8328bc147e41

DOOGLAK/Tagged_Uni_500v1_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

transformers

token-classification

apache-2.0

['tagged_uni500v1_wikigold_split']

['generated_from_trainer']

<!-- 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. --> # Tagged_Uni_500v1_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v1_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2425 - Precision: 0.7049 - Recall: 0.7077 - F1: 0.7063 - Accuracy: 0.9309 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 163 | 0.2804 | 0.4476 | 0.3886 | 0.4160 | 0.9019 | | No log | 2.0 | 326 | 0.2401 | 0.6803 | 0.6657 | 0.6729 | 0.9265 | | No log | 3.0 | 489 | 0.2425 | 0.7049 | 0.7077 | 0.7063 | 0.9309 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

073fb1841a11634dd733e6c657dd9bff

arielazzi/whisper-small-pt

arielazzi

whisper

transformers

automatic-speech-recognition

apache-2.0

['pt']

['mozilla-foundation/common_voice_11_0']

['hf-asr-leaderboard', 'generated_from_trainer']

<!-- 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. --> # Whisper Small PT - Ariel Azzi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2065 - Wer: 14.3447 ## 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: 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: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.198 | 0.59 | 1000 | 0.2338 | 16.2424 | | 0.0933 | 1.19 | 2000 | 0.2138 | 14.9756 | | 0.082 | 1.78 | 3000 | 0.2024 | 14.2111 | | 0.0452 | 2.38 | 4000 | 0.2065 | 14.3447 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

028b1c270a57bbdc3114808b6c4d8c0f

osanseviero/tipsuhtxfu-sex-classification

osanseviero

sklearn

tabular-classification

apache-2.0

['tabular-classification', 'baseline-trainer']

## Baseline Model trained on tipsuhtxfu to apply classification on sex **Metrics of the best model:** accuracy 0.647364 average_precision 0.507660 roc_auc 0.625546 recall_macro 0.589832 f1_macro 0.585292 Name: MultinomialNB(), dtype: float64 **See model plot below:** <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt

2644201c3083524acefdc7536c877e6e

snap-research/efficientformer-l1-300

snap-research

efficientformer

timm

image-classification

apache-2.0

['en']

['imagenet-1k']

['mobile', 'vison', 'image-classification']

# EfficientFormer-L1 ## Table of Contents - [EfficientFormer-L1](#-model_id--defaultmymodelname-true) - [Table of Contents](#table-of-contents) - [Model Details](#model-details) - [How to Get Started with the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Direct Use](#direct-use) - [Downstream Use](#downstream-use) - [Misuse and Out-of-scope Use](#misuse-and-out-of-scope-use) - [Limitations and Biases](#limitations-and-biases) - [Training](#training) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Evaluation Results](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Citation Information](#citation-information) <model_details> ## Model Details <!-- Give an overview of your model, the relevant research paper, who trained it, etc. --> EfficientFormer-L1, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. This checkpoint of EfficientFormer-L1 was trained for 1000 epochs. - Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren - Language(s): English - License: This model is licensed under the apache-2.0 license - Resources for more information: - [Research Paper](https://arxiv.org/abs/2206.01191) - [GitHub Repo](https://github.com/snap-research/EfficientFormer/) </model_details> <how_to_start> ## How to Get Started with the Model Use the code below to get started with the model. ```python import requests import torch from PIL import Image from transformers import EfficientFormerImageProcessor, EfficientFormerForImageClassificationWithTeacher # Load a COCO image of two cats to test the model url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # Load preprocessor and pretrained model model_name = "huggingface/efficientformer-l1-300" processor = EfficientFormerImageProcessor.from_pretrained(model_name) model = EfficientFormerForImageClassificationWithTeacher.from_pretrained(model_name) # Preprocess input image inputs = processor(images=image, return_tensors="pt") # Inference with torch.no_grad(): outputs = model(**inputs) # Print the top ImageNet1k class prediction logits = outputs.logits scores = torch.nn.functional.softmax(logits, dim=1) top_pred_class = torch.argmax(scores, dim=1) print(f"Predicted class: {top_pred_class}") ``` </how_to_start> <uses> ## Uses #### Direct Use This model can be used for image classification and semantic segmentation. On mobile devices (the model was tested on iPhone 12), the CoreML checkpoints will perform these tasks with low latency. <Limitations_and_Biases> ## Limitations and Biases Though most designs in EfficientFormer are general-purposed, e.g., dimension- consistent design and 4D block with CONV-BN fusion, the actual speed of EfficientFormer may vary on other platforms. For instance, if GeLU is not well supported while HardSwish is efficiently implemented on specific hardware and compiler, the operator may need to be modified accordingly. The proposed latency-driven slimming is simple and fast. However, better results may be achieved if search cost is not a concern and an enumeration-based brute search is performed. Since the model was trained on Imagenet-1K, the [biases embedded in that dataset](https://huggingface.co/datasets/imagenet-1k#considerations-for-using-the-data) will be reflected in the EfficientFormer models. </Limitations_and_Biases> <Training> ## Training #### Training Data This model was trained on ImageNet-1K. See the [data card](https://huggingface.co/datasets/imagenet-1k) for additional information. #### Training Procedure * Parameters: 12.3 M * GMACs: 1.3 * Train. Epochs: 1000 Trained on a cluster with NVIDIA A100 and V100 GPUs. </Training> <Eval_Results> ## Evaluation Results Top-1 Accuracy: 80.2% on ImageNet 10K </Eval_Results> <Cite> ## Citation Information ```bibtex @article{li2022efficientformer, title={EfficientFormer: Vision Transformers at MobileNet Speed}, author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian}, journal={arXiv preprint arXiv:2206.01191}, year={2022} } ``` </Cite>

0805f4ba7f4cc54e5338563593fd78d8

shamikbose89/mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full

shamikbose89

mt5

transformers

summarization

apache-2.0

['generated_from_trainer', 'summarization']

<!-- 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-finetuned-arxiv-cs-finetuned-arxiv-cs-full This model is a fine-tuned version of [shamikbose89/mt5-small-finetuned-arxiv-cs](https://huggingface.co/shamikbose89/mt5-small-finetuned-arxiv-cs) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4037 - Rouge1: 39.8923 - Rouge2: 20.9831 - Rougel: 35.8642 - Rougelsum: 35.8511 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.9675 | 1.0 | 500 | 1.5573 | 36.4989 | 18.4839 | 33.2984 | 33.2917 | | 1.7523 | 2.0 | 1000 | 1.4972 | 37.7911 | 19.0357 | 33.5725 | 33.6058 | | 1.6611 | 3.0 | 1500 | 1.4593 | 38.5822 | 19.4928 | 34.215 | 34.2531 | | 1.6187 | 4.0 | 2000 | 1.4492 | 39.1219 | 20.8705 | 35.1969 | 35.2255 | | 1.5864 | 5.0 | 2500 | 1.4289 | 39.7304 | 21.0654 | 35.6602 | 35.6667 | | 1.5553 | 6.0 | 3000 | 1.4184 | 40.0696 | 21.0883 | 35.9536 | 35.9132 | | 1.5215 | 7.0 | 3500 | 1.4163 | 39.1956 | 20.6757 | 35.5016 | 35.5196 | | 1.5038 | 8.0 | 4000 | 1.4148 | 39.2373 | 20.3114 | 35.1676 | 35.1532 | | 1.4929 | 9.0 | 4500 | 1.4064 | 39.9249 | 21.0155 | 35.8247 | 35.7937 | | 1.4791 | 10.0 | 5000 | 1.4037 | 39.8923 | 20.9831 | 35.8642 | 35.8511 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

e82f4fa7c1453743a091fcbaf30ec210

omar47/wav2vec2-large-xls-r-300m-urdu

omar47

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- 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-xls-r-300m-urdu This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m). It achieves the following results on the evaluation set: - Loss: 0.5285 - Wer: 0.1702 ## 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: 35 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 16.9618 | 0.74 | 32 | 15.0745 | 1.0 | | 9.1928 | 1.49 | 64 | 5.9361 | 1.0 | | 4.9307 | 2.23 | 96 | 4.2924 | 1.0 | | 3.8917 | 2.98 | 128 | 3.5873 | 1.0 | | 3.3867 | 3.72 | 160 | 3.2594 | 1.0 | | 3.2107 | 4.47 | 192 | 3.1718 | 1.0 | | 3.1395 | 5.21 | 224 | 3.1281 | 1.0 | | 3.115 | 5.95 | 256 | 3.1238 | 1.0 | | 3.0801 | 6.7 | 288 | 3.0674 | 1.0 | | 2.9725 | 7.44 | 320 | 2.8277 | 1.0 | | 2.4159 | 8.19 | 352 | 1.7186 | 0.9036 | | 1.3377 | 8.93 | 384 | 1.0271 | 0.6433 | | 0.8591 | 9.67 | 416 | 0.8087 | 0.5441 | | 0.726 | 10.42 | 448 | 0.7263 | 0.4634 | | 0.6242 | 11.16 | 480 | 0.6783 | 0.4156 | | 0.5417 | 11.91 | 512 | 0.6611 | 0.4305 | | 0.4784 | 12.65 | 544 | 0.6300 | 0.3926 | | 0.4198 | 13.4 | 576 | 0.5646 | 0.3499 | | 0.3798 | 14.14 | 608 | 0.5919 | 0.3229 | | 0.3356 | 14.88 | 640 | 0.5715 | 0.3369 | | 0.2954 | 15.63 | 672 | 0.5325 | 0.2728 | | 0.264 | 16.37 | 704 | 0.5535 | 0.2689 | | 0.2535 | 17.12 | 736 | 0.5467 | 0.2366 | | 0.2277 | 17.86 | 768 | 0.5219 | 0.2345 | | 0.2141 | 18.6 | 800 | 0.5314 | 0.2487 | | 0.2036 | 19.35 | 832 | 0.5382 | 0.2236 | | 0.2021 | 20.09 | 864 | 0.5038 | 0.1922 | | 0.1676 | 20.84 | 896 | 0.5238 | 0.2033 | | 0.1544 | 21.58 | 928 | 0.5069 | 0.1866 | | 0.1512 | 22.33 | 960 | 0.5045 | 0.1965 | | 0.1512 | 23.07 | 992 | 0.5167 | 0.1862 | | 0.1399 | 23.81 | 1024 | 0.5236 | 0.1840 | | 0.1291 | 24.56 | 1056 | 0.5234 | 0.1957 | | 0.1274 | 25.3 | 1088 | 0.5348 | 0.1943 | | 0.127 | 26.05 | 1120 | 0.4978 | 0.1719 | | 0.1105 | 26.79 | 1152 | 0.5067 | 0.1767 | | 0.1069 | 27.53 | 1184 | 0.5150 | 0.1758 | | 0.1058 | 28.28 | 1216 | 0.5218 | 0.1844 | | 0.0999 | 29.02 | 1248 | 0.5375 | 0.1852 | | 0.0964 | 29.77 | 1280 | 0.5373 | 0.1843 | | 0.0971 | 30.51 | 1312 | 0.5190 | 0.1776 | | 0.0906 | 31.26 | 1344 | 0.5217 | 0.1747 | | 0.0909 | 32.0 | 1376 | 0.5204 | 0.1778 | | 0.0784 | 32.74 | 1408 | 0.5336 | 0.1756 | | 0.0823 | 33.49 | 1440 | 0.5281 | 0.1699 | | 0.0834 | 34.23 | 1472 | 0.5292 | 0.1700 | | 0.0827 | 34.98 | 1504 | 0.5285 | 0.1702 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

0b6dd712ca20eaee3dd07f1b9132fcbf

PlanTL-GOB-ES/roberta-large-bne-capitel-pos

PlanTL-GOB-ES

roberta

transformers

token-classification

apache-2.0

['es']

['bne', 'capitel']

['national library of spain', 'spanish', 'bne', 'capitel', 'pos']

# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset ## Table of contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Evaluation](#evaluation) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description The **roberta-large-bne-capitel-pos** is a Part-of-speech-tagging (POS) model for the Spanish language fine-tuned from the [roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) large model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. # Intended uses and limitations **roberta-large-bne-capitel-pos** model can be used to Part-of-speech-tagging (POS) a text. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("token-classification", model="PlanTL-GOB-ES/roberta-large-bne-capitel-pos") example = "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto." pos_results = nlp(example) pprint(pos_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2). ### Training procedure The model was trained with a batch size of 16 and a learning rate of 3e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 score. ## Evaluation results We evaluated the **roberta-large-bne-capitel-pos** on the CAPITEL-POS test set against standard multilingual and monolingual baselines: | Model | CAPITEL-POS (F1) | | ------------|:----| | roberta-large-bne-capitel-pos | **98.56** | | roberta-base-bne-capitel-pos | 98.46 | | BETO | 98.36 | | mBERT | 98.39 | | BERTIN | 98.47 | | ELECTRA | 98.16 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) ### Contact information For further information, send an email to <plantl-gob-es@bsc.es> ### Copyright Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. ### Citing information If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405): ``` @article{, abstract = {We want to thank the National Library of Spain for such a large effort on the data gathering and the Future of Computing Center, a Barcelona Supercomputing Center and IBM initiative (2020). This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.}, author = {Asier Gutiérrez Fandiño and Jordi Armengol Estapé and Marc Pàmies and Joan Llop Palao and Joaquin Silveira Ocampo and Casimiro Pio Carrino and Carme Armentano Oller and Carlos Rodriguez Penagos and Aitor Gonzalez Agirre and Marta Villegas}, doi = {10.26342/2022-68-3}, issn = {1135-5948}, journal = {Procesamiento del Lenguaje Natural}, keywords = {Artificial intelligence,Benchmarking,Data processing.,MarIA,Natural language processing,Spanish language modelling,Spanish language resources,Tractament del llenguatge natural (Informàtica),Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural}, publisher = {Sociedad Española para el Procesamiento del Lenguaje Natural}, title = {MarIA: Spanish Language Models}, volume = {68}, url = {https://upcommons.upc.edu/handle/2117/367156#.YyMTB4X9A-0.mendeley}, year = {2022}, } ``` ### Disclaimer The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models. Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables. Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial. En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.

c0b10f1b3fc9b448974289e9a6d8ff87

smeoni/nbme-xlnet-large-cased

smeoni

xlnet

transformers

text-generation

mit

['generated_from_trainer']

<!-- 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. --> # nbme-xlnet-large-cased This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7151 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.2931 | 1.0 | 1850 | 1.9915 | | 1.9467 | 2.0 | 3700 | 1.7866 | | 1.7983 | 3.0 | 5550 | 1.6919 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

8edb311b32423a0fbda03fff2535bd70

reinoudbosch/xlm-roberta-base-finetuned-panx-it

reinoudbosch

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- 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-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2527 - F1: 0.8086 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8319 | 1.0 | 70 | 0.3179 | 0.7474 | | 0.2959 | 2.0 | 140 | 0.2695 | 0.7916 | | 0.2036 | 3.0 | 210 | 0.2527 | 0.8086 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.0

c7508a66c903c447a571a32780dbd97e

Lowin/chinese-bigbird-tiny-1024

Lowin

big_bird

transformers

feature-extraction

['apache-2.0']

['zh']

[]

```python import jieba_fast from transformers import BertTokenizer from transformers import BigBirdModel class JiebaTokenizer(BertTokenizer): def __init__( self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs ): super().__init__(*args, **kwargs) self.pre_tokenizer = pre_tokenizer def _tokenize(self, text, *arg, **kwargs): split_tokens = [] for text in self.pre_tokenizer(text): if text in self.vocab: split_tokens.append(text) else: split_tokens.extend(super()._tokenize(text)) return split_tokens model = BigBirdModel.from_pretrained('Lowin/chinese-bigbird-tiny-1024') tokenizer = JiebaTokenizer.from_pretrained('Lowin/chinese-bigbird-tiny-1024') ``` https://github.com/LowinLi/chinese-bigbird

bc133d1192074b2e8c0fb1fc5ac414de

Gabriel/flan-t5-base-squad2-swe

Gabriel

t5

transformers

text2text-generation

apache-2.0

['squad_v2_sv']

['generated_from_trainer']

<!-- 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. --> # flan-t5-base-squad-swe2 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the squad_v2_sv dataset. It achieves the following results on the evaluation set: - Loss: 1.4248 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0881 | 1.0 | 890 | 1.6422 | | 1.7772 | 2.0 | 1780 | 1.5586 | | 1.6763 | 3.0 | 2670 | 1.5153 | | 1.6215 | 4.0 | 3560 | 1.4852 | | 1.5912 | 5.0 | 4450 | 1.4629 | | 1.5651 | 6.0 | 5340 | 1.4481 | | 1.5407 | 7.0 | 6230 | 1.4374 | | 1.5278 | 8.0 | 7120 | 1.4308 | | 1.5137 | 9.0 | 8010 | 1.4269 | | 1.5116 | 10.0 | 8900 | 1.4248 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

85930ea53f7984c1f31bd61c0ec37aea

vutankiet2901/wav2vec2-large-xlsr-53-ja

vutankiet2901

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['ja']

['mozilla-foundation/common_voice_8_0']

['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event']

## Model description This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset. ### Benchmark WER result: | | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |---|---|---| |without LM| 15.74 | 25.10 | |with 4-grams LM| 15.37 | 16.09 | ### Benchmark CER result: | | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |---|---|---| |without LM| 9.51 | 9.95 | |with 4-grams LM| 6.91 | 7.15 | ## Evaluation Please use the eval.py file to run the evaluation: ```python python eval.py --model_id vutankiet2901/wav2vec2-large-xlsr-53-ja --dataset mozilla-foundation/common_voice_7_0 --config ja --split test --log_outputs ``` ## 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: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 4.7776 | 4.73 | 1500 | 2.9540 | 0.9772 | 0.8489 | | 1.9076 | 9.46 | 3000 | 0.7146 | 0.5371 | 0.2484 | | 1.507 | 14.2 | 4500 | 0.5843 | 0.4689 | 0.2196 | | 1.3742 | 18.93 | 6000 | 0.5286 | 0.4321 | 0.1988 | | 1.2776 | 23.66 | 7500 | 0.5007 | 0.4056 | 0.1870 | | 1.2003 | 28.39 | 9000 | 0.4676 | 0.3848 | 0.1802 | | 1.1281 | 33.12 | 10500 | 0.4524 | 0.3694 | 0.1720 | | 1.0657 | 37.85 | 12000 | 0.4449 | 0.3590 | 0.1681 | | 1.0129 | 42.59 | 13500 | 0.4266 | 0.3423 | 0.1617 | | 0.9691 | 47.32 | 15000 | 0.4214 | 0.3375 | 0.1587 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

33b610128c1df8542a2b4bca0a0a3c75

shi-labs/versatile-diffusion

shi-labs

diffusers

text-to-image

mit

['Laion2B-en']

['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']

# Versatile Diffusion V1.0 Model Card We built **Versatile Diffusion (VD), the first unified multi-flow multimodal diffusion framework**, as a step towards **Universal Generative AI**. Versatile Diffusion can natively support image-to-text, image-variation, text-to-image, and text-variation, and can be further extended to other applications such as semantic-style disentanglement, image-text dual-guided generation, latent image-to-text-to-image editing, and more. Future versions will support more modalities such as speech, music, video and 3D. Resources for more information: [GitHub](https://github.com/SHI-Labs/Versatile-Diffusion), [arXiv](https://arxiv.org/abs/2211.08332). # Model Details One single flow of Versatile Diffusion contains a VAE, a diffuser, and a context encoder, and thus handles one task (e.g., text-to-image) under one data type (e.g., image) and one context type (e.g., text). The multi-flow structure of Versatile Diffusion shows in the following diagram: <p align="center"> <img src="https://huggingface.co/shi-labs/versatile-diffusion-model/resolve/main/assets/figures/vd_combined.png" width="99%"> </p> - **Developed by:** Xingqian Xu, Atlas Wang, Eric Zhang, Kai Wang, and Humphrey Shi - **Model type:** Diffusion-based multimodal generation model - **Language(s):** English - **License:** MIT - **Resources for more information:** [GitHub Repository](https://github.com/SHI-Labs/Versatile-Diffusion), [Paper](https://arxiv.org/abs/2211.08332). - **Cite as:** ``` @article{xu2022versatile, title = {Versatile Diffusion: Text, Images and Variations All in One Diffusion Model}, author = {Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2211.08332}, eprint = {2211.08332}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ``` # Usage You can use the model both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [SHI-Labs Versatile Diffusion codebase](https://github.com/SHI-Labs/Versatile-Diffusion). ## 🧨 Diffusers Diffusers let's you both use a unified and more memory-efficient, task-specific pipelines. **Make sure to install `transformers` from `"main"` in order to use this model.**: ``` pip install git+https://github.com/huggingface/transformers ``` ## VersatileDiffusionPipeline To use Versatile Diffusion for all tasks, it is recommend to use the [`VersatileDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#diffusers.VersatileDiffusionPipeline) ```py #! pip install git+https://github.com/huggingface/transformers diffusers torch from diffusers import VersatileDiffusionPipeline import torch import requests from io import BytesIO from PIL import Image pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") # prompt prompt = "a red car" # initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") # text to image image = pipe.text_to_image(prompt).images[0] # image variation image = pipe.image_variation(image).images[0] # image variation image = pipe.dual_guided(prompt, image).images[0] ``` ### Task Specific The task specific pipelines load only the weights that are needed onto GPU. You can find all task specific pipelines [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#versatilediffusion). You can use them as follows: ### Text to Image ```py from diffusers import VersatileDiffusionTextToImagePipeline import torch pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0] image.save("./astronaut.png") ``` #### Image variations ```py from diffusers import VersatileDiffusionImageVariationPipeline import torch import requests from io import BytesIO from PIL import Image # download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe(image, generator=generator).images[0] image.save("./car_variation.png") ``` #### Dual-guided generation ```py from diffusers import VersatileDiffusionDualGuidedPipeline import torch import requests from io import BytesIO from PIL import Image # download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") text = "a red car in the sun" pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) text_to_image_strength = 0.75 image = pipe(prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator).images[0] image.save("./red_car.png") ``` ### Original GitHub Repository Follow the instructions [here](https://github.com/SHI-Labs/Versatile-Diffusion/#evaluation). # Cautions, Biases, and Content Acknowledgment We would like the raise the awareness of users of this demo of its potential issues and concerns. Like previous large foundation models, Versatile Diffusion could be problematic in some cases, partially due to the imperfect training data and pretrained network (VAEs / context encoders) with limited scope. In its future research phase, VD may do better on tasks such as text-to-image, image-to-text, etc., with the help of more powerful VAEs, more sophisticated network designs, and more cleaned data. So far, we have kept all features available for research testing both to show the great potential of the VD framework and to collect important feedback to improve the model in the future. We welcome researchers and users to report issues with the HuggingFace community discussion feature or email the authors. Beware that VD may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. VD was trained on the LAION-2B dataset, which scraped non-curated online images and text, and may contain unintended exceptions as we removed illegal content. VD in this demo is meant only for research purposes.

81ef62a79711128b12689633798f1686

ali2066/finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22

ali2066

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- 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. --> # finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0890 - Accuracy: 0.9750 - F1: 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: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 104 | 0.0485 | 0.9885 | 0.9942 | | No log | 2.0 | 208 | 0.0558 | 0.9857 | 0.9927 | | No log | 3.0 | 312 | 0.0501 | 0.9828 | 0.9913 | | No log | 4.0 | 416 | 0.0593 | 0.9828 | 0.9913 | | 0.04 | 5.0 | 520 | 0.0653 | 0.9828 | 0.9913 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

5d76404467713ea741bec8eb4969f068

YaHi/bert-base-uncased-finetuned-effectiveFeedback

YaHi

bert

transformers

fill-mask

apache-2.0

['generated_from_trainer']

<!-- 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-base-uncased-finetuned-effectiveFeedback This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 361 | 0.0003 | | 0.0139 | 2.0 | 722 | 0.0001 | | 0.0002 | 3.0 | 1083 | 0.0001 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

38d312d9f9558de944f31922be3c8251

bitextor/bicleaner-ai-full-en-sq

bitextor

xlm-roberta

transformers

gpl-3.0

['en', 'sq', 'multilingual']

['bicleaner-ai']

# Bicleaner AI full model for en-sq Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0). Sentence pairs considered very noisy are scored with 0. Find out at our repository for further instructions on how to use it: https://github.com/bitextor/bicleaner-ai

ed210af26b32d90af80a6de8085f838d

MultiBertGunjanPatrick/multiberts-seed-4-300k

MultiBertGunjanPatrick

bert

transformers

apache-2.0

['en']

['bookcorpus', 'wikipedia']

['exbert', 'multiberts', 'multiberts-seed-4']

# MultiBERTs Seed 4 Checkpoint 300k (uncased) Seed 4 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-4](https://hf.co/multberts-seed-4). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-300k') model = BertModel.from_pretrained("multiberts-seed-4-300k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

56dfd0de4a03b807d28401d579dc5ce9

cahya/wav2vec2-large-xlsr-indonesian-artificial

cahya

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['id']

['common_voice']

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

# Wav2Vec2-Large-XLSR-Indonesian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Indonesian Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181). 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", "id", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") 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[:2]["speech"], 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[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the Indonesian 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", "id", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]' 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): 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**: 51.69 % ## Training The Artificial Common Voice `train`, `validation`, and ... datasets were used for training. The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition) (will be available soon)

254a1e8a6b55b2bac824935215a2e043

RecordedFuture/Swedish-Sentiment-Fear

RecordedFuture

bert

transformers

text-classification

mit

['sv']

[]

## Swedish BERT models for sentiment analysis [Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for sentiment analysis in Swedish. The two models are based on the [KB\/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) model and has been fine-tuned to solve a multi-label sentiment analysis task. The models have been fine-tuned for the sentiments fear and violence. The models output three floats corresponding to the labels "Negative", "Weak sentiment", and "Strong Sentiment" at the respective indexes. The models have been trained on Swedish data with a conversational focus, collected from various internet sources and forums. The models are only trained on Swedish data and only supports inference of Swedish input texts. The models inference metrics for all non-Swedish inputs are not defined, these inputs are considered as out of domain data. The current models are supported at Transformers version >= 4.3.3 and Torch version 1.8.0, compatibility with older versions are not verified. ### Swedish-Sentiment-Fear The model can be imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear") classifier_fear= BertForSequenceClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear") When the model and tokenizer are initialized the model can be used for inference. #### Sentiment definitions #### The strong sentiment includes but are not limited to Texts that: - Hold an expressive emphasis on fear and/ or anxiety #### The weak sentiment includes but are not limited to Texts that: - Express fear and/ or anxiety in a neutral way #### Verification metrics During training, the model had maximized validation metrics at the following classification breakpoint. | Classification Breakpoint | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.45 | 0.8754 | 0.8618 | 0.8895 | #### Swedish-Sentiment-Violence The model be can imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence") classifier_violence = BertForSequenceClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence") When the model and tokenizer are initialized the model can be used for inference. ### Sentiment definitions #### The strong sentiment includes but are not limited to Texts that: - Referencing highly violent acts - Hold an aggressive tone #### The weak sentiment includes but are not limited to Texts that: - Include general violent statements that do not fall under the strong sentiment #### Verification metrics During training, the model had maximized validation metrics at the following classification breakpoint. | Classification Breakpoint | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.35 | 0.7677 | 0.7456 | 0.791 |

f541ee2147d9b1b712e98ac0f6acdda1

Geotrend/distilbert-base-en-ja-cased

Geotrend

distilbert

transformers

fill-mask

apache-2.0

['multilingual']

['wikipedia']

[]

# distilbert-base-en-ja-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-ja-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-ja-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

a0e7ef7332587714f4a84a5de45535ce

Helsinki-NLP/opus-mt-it-ca

Helsinki-NLP

marian

transformers

translation

apache-2.0

['it', 'ca']

['translation']

### ita-cat * source group: Italian * target group: Catalan * OPUS readme: [ita-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-cat/README.md) * model: transformer-align * source language(s): ita * target language(s): cat * model: transformer-align * pre-processing: normalization + SentencePiece (spm12k,spm12k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ita.cat | 52.5 | 0.706 | ### System Info: - hf_name: ita-cat - source_languages: ita - target_languages: cat - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-cat/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'ca'] - src_constituents: {'ita'} - tgt_constituents: {'cat'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm12k,spm12k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.test.txt - src_alpha3: ita - tgt_alpha3: cat - short_pair: it-ca - chrF2_score: 0.706 - bleu: 52.5 - brevity_penalty: 0.993 - ref_len: 2074.0 - src_name: Italian - tgt_name: Catalan - train_date: 2020-06-16 - src_alpha2: it - tgt_alpha2: ca - prefer_old: False - long_pair: ita-cat - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

fa45c09a72d2ca1340c9570100afdf3f

gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_stsb

gokuls

mobilebert

transformers

text-classification

apache-2.0