Datasets:
davanstrien
/
model_cards_with_readmes

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Kuray107/librispeech-100h-supervised-meta
Kuray107
wav2vec2
7
6
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
null
null
null
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0
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0
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0
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['generated_from_trainer']
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2,280
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<!-- 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. --> # librispeech-100h-supervised-meta This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.co/Kuray107/librispeech-5h-supervised) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0965 - Wer: 0.0330 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1131 | 1.12 | 1000 | 0.0755 | 0.0487 | | 0.0725 | 2.24 | 2000 | 0.0637 | 0.0404 | | 0.0539 | 3.36 | 3000 | 0.0661 | 0.0389 | | 0.0441 | 4.48 | 4000 | 0.0637 | 0.0371 | | 0.0379 | 5.61 | 5000 | 0.0675 | 0.0356 | | 0.0341 | 6.73 | 6000 | 0.0735 | 0.0360 | | 0.0295 | 7.85 | 7000 | 0.0737 | 0.0362 | | 0.0265 | 8.97 | 8000 | 0.0741 | 0.0350 | | 0.0244 | 10.09 | 9000 | 0.0779 | 0.0337 | | 0.0217 | 11.21 | 10000 | 0.0835 | 0.0343 | | 0.0203 | 12.33 | 11000 | 0.0785 | 0.0339 | | 0.0188 | 13.45 | 12000 | 0.0827 | 0.0344 | | 0.0179 | 14.57 | 13000 | 0.0875 | 0.0332 | | 0.0169 | 15.7 | 14000 | 0.0860 | 0.0330 | | 0.0158 | 16.82 | 15000 | 0.0954 | 0.0330 | | 0.0147 | 17.94 | 16000 | 0.0934 | 0.0329 | | 0.0148 | 19.06 | 17000 | 0.0965 | 0.0330 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
79d9aaa2219667af98a8d4c2a8d54a6d
muhtasham/tiny-mlm-glue-rte-custom-tokenizer
muhtasham
bert
12
0
transformers
1
fill-mask
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
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1,358
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<!-- 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. --> # tiny-mlm-glue-rte-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.3646 ## 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 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.71 | 1.6 | 500 | 7.1503 | | 6.8618 | 3.21 | 1000 | 7.2787 | | 6.816 | 4.81 | 1500 | 7.2543 | | 6.7094 | 6.41 | 2000 | 7.3646 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
04eb3d89b72f40c2b1b56a79ea92f755
lmqg/t5-large-subjqa-tripadvisor-qg
lmqg
t5
34
1
transformers
0
text2text-generation
true
false
false
cc-by-4.0
['en']
['lmqg/qg_subjqa']
null
0
0
0
0
0
0
0
['question generation']
true
true
true
4,016
false
# Model Card of `lmqg/t5-large-subjqa-tripadvisor-qg` This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (tripadvisor) - **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="en", model="lmqg/t5-large-subjqa-tripadvisor-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-large-subjqa-tripadvisor-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-subjqa-tripadvisor-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | | Score | Type | Dataset | |:-----------|--------:|:------------|:-----------------------------------------------------------------| | BERTScore | 94.46 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 26.44 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 17.84 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 9.13 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 5.35 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 27.45 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 67.76 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 27.69 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: tripadvisor - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-large-squad - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 16 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-subjqa-tripadvisor-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", } ```
f16ff422f4eeed8070d503b8e41bc8ee
CLAck/en-vi
CLAck
marian
11
25
transformers
0
translation
true
false
false
apache-2.0
['en', 'vi']
['ALT']
null
1
0
0
1
0
0
0
['translation']
false
true
true
1,748
false
This is a finetuning of a MarianMT pretrained on English-Chinese. The target language pair is English-Vietnamese. The first phase of training (mixed) is performed on a dataset containing both English-Chinese and English-Vietnamese sentences. The second phase of training (pure) is performed on a dataset containing only English-Vietnamese sentences. ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/en-vi") tokenizer = AutoTokenizer.from_pretrained("CLAck/en-vi") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2vi>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2vi> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` ### Training results MIXED | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 26.2407 | | 2.0 | 32.6016 | | 3.0 | 35.4060 | | 4.0 | 36.6737 | | 5.0 | 37.3774 | PURE | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 37.3169 | | 2.0 | 37.4407 | | 3.0 | 37.6696 | | 4.0 | 37.8765 | | 5.0 | 38.0105 |
29a961ec256467dd85a5d89310a531d1
CVPR/FSPBT
CVPR
null
3
0
PyTorch Lightning
0
null
false
false
false
mit
null
null
null
0
0
0
0
0
0
0
['Image Translation']
false
true
true
599
false
## Model Details This model is from [FSPBT-Image-Translation](https://github.com/rnwzd/FSPBT-Image-Translation) ## Citation Information ```bibtex @Article{Texler20-SIG, author = "Ond\v{r}ej Texler and David Futschik and Michal Ku\v{c}era and Ond\v{r}ej Jamri\v{s}ka and \v{S}\'{a}rka Sochorov\'{a} and Menglei Chai and Sergey Tulyakov and Daniel S\'{y}kora", title = "Interactive Video Stylization Using Few-Shot Patch-Based Training", journal = "ACM Transactions on Graphics", volume = "39", number = "4", pages = "73", year = "2020", } ```
464e0e8ce5078d1f3ad9a684cd9ab127
Helsinki-NLP/opus-mt-sv-ilo
Helsinki-NLP
marian
10
7
transformers
0
translation
true
true
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['translation']
false
true
true
776
false
### opus-mt-sv-ilo * source languages: sv * target languages: ilo * OPUS readme: [sv-ilo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ilo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-ilo/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ilo/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ilo/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.ilo | 34.8 | 0.578 |
31bc1bd048632b4a7f8635fccaa22619
monologg/koelectra-base-generator
monologg
electra
6
47
transformers
0
fill-mask
true
false
false
apache-2.0
['ko']
null
null
0
0
0
0
0
0
0
['korean']
false
true
true
1,309
false
# KoELECTRA (Base Generator) Pretrained ELECTRA Language Model for Korean (`koelectra-base-generator`) For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md). ## Usage ### Load model and tokenizer ```python >>> from transformers import ElectraModel, ElectraTokenizer >>> model = ElectraModel.from_pretrained("monologg/koelectra-base-generator") >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator") ``` ### Tokenizer example ```python >>> from transformers import ElectraTokenizer >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator") >>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]") ['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]'] >>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]']) [2, 18429, 41, 6240, 15229, 6204, 20894, 5689, 12622, 10690, 18, 3] ``` ## Example using ElectraForMaskedLM ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="monologg/koelectra-base-generator", tokenizer="monologg/koelectra-base-generator" ) print(fill_mask("나는 {} 밥을 먹었다.".format(fill_mask.tokenizer.mask_token))) ```
88172dba26ef2bdf51cfcdd0c7b1cc72
mastergruffly/profile
mastergruffly
null
18
7
diffusers
0
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
1
1
0
0
0
0
0
['text-to-image', 'stable-diffusion']
false
true
true
615
false
### profile Dreambooth model trained by mastergruffly 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) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:
464476adf653f55519a15e38e4997e33
pyf98/voxforge_it_conformer_e12_linear2048
pyf98
null
21
2
espnet
0
automatic-speech-recognition
false
false
false
cc-by-4.0
['it']
['voxforge']
null
0
0
0
0
0
0
0
['espnet', 'audio', 'automatic-speech-recognition']
false
true
true
6,790
false
## ESPnet2 ASR model ### `pyf98/voxforge_it_conformer_e12_linear2048` This model was trained by Yifan Peng using voxforge recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout bf8c8f00194bdfed8ca388d8b20d14791b7d270e pip install -e . cd egs2/voxforge/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/voxforge_it_conformer_e12_linear2048 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Thu Dec 29 01:45:02 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `bf8c8f00194bdfed8ca388d8b20d14791b7d270e` - Commit date: `Wed Dec 28 22:43:13 2022 -0500` ## asr_train_asr_conformer_e12_linear2048_raw_it_char_normalize_confnorm_varsFalse ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|12587|70.3|24.6|5.1|3.3|33.0|95.4| |decode_asr_asr_model_valid.acc.ave/et_it|1103|13699|72.4|22.5|5.1|2.9|30.5|91.5| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|75494|92.9|3.9|3.2|1.8|8.9|95.4| |decode_asr_asr_model_valid.acc.ave/et_it|1103|81228|93.7|3.5|2.8|1.7|8.0|91.5| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e12_linear2048.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e12_linear2048_raw_it_char_normalize_confnorm_varsFalse ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 128 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_it_char/train/speech_shape - exp/asr_stats_raw_it_char/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_it_char/valid/speech_shape - exp/asr_stats_raw_it_char/valid/text_shape.char batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr_it/wav.scp - speech - sound - - dump/raw/tr_it/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dt_it/wav.scp - speech - sound - - dump/raw/dt_it/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 scheduler: warmuplr scheduler_conf: warmup_steps: 10000 token_list: - <blank> - <unk> - <space> - A - E - I - O - R - N - L - S - T - C - D - U - M - P - V - G - F - H - B - Q - Z - '''' - Ò - À - È - Ú - X - W - Í - É - Y - K - J - '1' - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: fs: 16k specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_it_char/train/feats_stats.npz norm_vars: false model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202211' distributed: false ``` </details> ### 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 Yalta 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} } ``` 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 Yalta 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} } ```
95aa7c1cff51e692965b85954a8e8a11
bongsoo/albert-small-kor-cross-encoder-v1
bongsoo
albert
8
123
transformers
0
text-classification
true
false
false
apache-2.0
['ko']
null
null
0
0
0
0
0
0
0
[]
false
true
true
1,916
false
# albert-small-kor-cross-encoder-v1 - albert-small-kor-v1 모델을 훈련시켜 cross-encoder로 파인튜닝한 모델 - This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. # Training - sts(10)-nli(3)-sts(10)-nli(3)-sts(10) 훈련 시킴 (**distil 훈련 없음**) - STS : seed=111,epoch=10, lr=1e-4, eps=1e-6, warm_step=10%, max_seq_len=128, train_batch=128(small 모델=32) (albert 13m/7G) [훈련코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-corossencoder-train-nli.ipynb) - NLI 훈련 : seed=111,epoch=3, lr=3e-5, eps=1e-8, warm_step=10%, max_seq_len=128, train_batch=64, eval_bath=64(albert 2h/7G) [훈련코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-corossencoder-train-sts.ipynb) - [평가코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-crossencoder-test3.ipynb),[테스트코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-crossencoder-test.ipynb) - |모델 |korsts|klue-sts|glue(stsb)|stsb_multi_mt(en)| |:--------|------:|--------:|--------------:|------------:| |**albert-small-kor-cross-encoder-v1** |0.8455 |0.8526 |0.8513 |0.7976| |klue-cross-encoder-v1 |0.8262 |0.8833 |0.8512 |0.7889| |kpf-cross-encoder-v1 |0.8799 |0.9133 |0.8626 |0.8027| ## Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('bongsoo/albert-small-kor-cross-encoder-v1') scores = model.predict([('오늘 날씨가 좋다', '오늘 등산을 한다'), ('오늘 날씨가 흐리다', '오늘 비가 내린다')]) print(scores) ``` ``` [0.45417202 0.6294121 ] ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
d08ef34d72b0a23a3c85e27d5b94db65
facebook/m2m100-12B-last-ckpt
facebook
m2m_100
9
676
transformers
1
text2text-generation
true
false
false
mit
['multilingual', 'af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', False, 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu']
null
null
1
0
1
0
0
0
0
['m2m100-12B']
false
true
true
3,999
false
# M2M100 12B M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. *Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.* To install `sentencepiece` run `pip install sentencepiece` ```python from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।" chinese_text = "生活就像一盒巧克力。" model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100-12B-last-ckpt") tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100-12B-last-ckpt") # translate Hindi to French tokenizer.src_lang = "hi" encoded_hi = tokenizer(hi_text, return_tensors="pt") generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "La vie est comme une boîte de chocolat." # translate Chinese to English tokenizer.src_lang = "zh" encoded_zh = tokenizer(chinese_text, return_tensors="pt") generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Life is like a box of chocolate." ``` See the [model hub](https://huggingface.co/models?filter=m2m_100) to look for more fine-tuned versions. ## Languages covered Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu) ## BibTeX entry and citation info ``` @misc{fan2020englishcentric, title={Beyond English-Centric Multilingual Machine Translation}, author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin}, year={2020}, eprint={2010.11125}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
c0eb5b950e8bbb7a70d452c10b0c9324
pere/norwegian-mt5
pere
t5
19
12
transformers
0
text2text-generation
false
false
true
cc-by-4.0
False
['Norwegian Nynorsk/Bokmål']
null
0
0
0
0
0
0
0
['seq2seq']
false
true
true
804
false
# 🇳🇴 Norwegian mT5 Base model 🇳🇴 This mT5-base model is trained from the mT5 checkpoint on a 19GB Balanced Bokmål-Nynorsk Corpus. Parameters used in training: ```bash python3 ./run_t5_mlm_flax_streaming.py --model_name_or_path="./norwegian-t5-base" --output_dir="./norwegian-t5-base" --config_name="./norwegian-t5-base" --tokenizer_name="./norwegian-t5-base" --dataset_name="pere/nb_nn_balanced_shuffled" --max_seq_length="512" --per_device_train_batch_size="32" --per_device_eval_batch_size="32" --learning_rate="0.005" --weight_decay="0.001" --warmup_steps="2000" --overwrite_output_dir --logging_steps="100" --save_steps="500" --eval_steps="500" --push_to_hub --preprocessing_num_workers 96 --adafactor ```
aaaaee3fa7a262bbb28f7b09f79fec02
anas-awadalla/roberta-large-few-shot-k-128-finetuned-squad-seed-0
anas-awadalla
roberta
17
3
transformers
0
question-answering
true
false
false
mit
null
['squad']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
986
false
<!-- 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-large-few-shot-k-128-finetuned-squad-seed-0 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 0 - 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 2.0.0 - Tokenizers 0.11.6
5683fc44762d42993e923c4839a5264e
paola-md/distilr2-lr2e05-wd0.05-bs64
paola-md
roberta
6
1
transformers
0
text-classification
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,519
false
<!-- 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. --> # distilr2-lr2e05-wd0.05-bs64 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2722 - Rmse: 0.5217 - Mse: 0.2722 - Mae: 0.4092 ## 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: 512 - eval_batch_size: 512 - 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 | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2772 | 1.0 | 312 | 0.2741 | 0.5236 | 0.2741 | 0.4238 | | 0.2737 | 2.0 | 624 | 0.2726 | 0.5221 | 0.2726 | 0.4078 | | 0.2718 | 3.0 | 936 | 0.2727 | 0.5222 | 0.2727 | 0.4146 | | 0.2697 | 4.0 | 1248 | 0.2722 | 0.5217 | 0.2722 | 0.4092 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1
73d48c32ceeb34906a21d1a62b8d72e7
lmqg/mt5-small-jaquad-qg-ae
lmqg
mt5
40
189
transformers
1
text2text-generation
true
false
false
cc-by-4.0
['ja']
['lmqg/qg_jaquad']
null
0
0
0
0
0
0
0
['question generation', 'answer extraction']
true
true
true
7,174
false
# Model Card of `lmqg/mt5-small-jaquad-qg-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ja - **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (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="ja", model="lmqg/mt5-small-jaquad-qg-ae") # model prediction question_answer_pairs = model.generate_qa("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-jaquad-qg-ae") # answer extraction answer = pipe("generate question: ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている<hl>6月28日<hl>は2人の14回目の結婚記念日であった。") # question generation question = pipe("extract answers: 『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 81.64 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_1 | 56.94 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_2 | 45.23 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_3 | 37.37 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_4 | 31.55 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | METEOR | 29.64 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | MoverScore | 59.42 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | ROUGE_L | 52.58 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_jaquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 80.51 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | QAAlignedF1Score (MoverScore) | 56.28 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | QAAlignedPrecision (BERTScore) | 80.51 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | QAAlignedPrecision (MoverScore) | 56.28 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | QAAlignedRecall (BERTScore) | 80.51 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | QAAlignedRecall (MoverScore) | 56.28 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_jaquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 29.55 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | AnswerF1Score | 29.55 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | BERTScore | 78.12 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_1 | 34.96 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_2 | 31.92 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_3 | 29.49 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_4 | 27.55 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | METEOR | 26.22 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | MoverScore | 65.68 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | ROUGE_L | 36.63 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_jaquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 24 - batch: 64 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-jaquad-qg-ae/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", } ```
65e098fcb0afd8f1053bcd3b228048af
ArafatBHossain/robert_base_fine_tuned_emotion_dataset
ArafatBHossain
roberta
10
3
transformers
0
text-classification
true
false
false
mit
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,322
false
<!-- 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. --> # robert_base_fine_tuned_emotion_dataset This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1996 - Accuracy: 0.936 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3438 | 1.0 | 2000 | 0.3140 | 0.921 | | 0.1911 | 2.0 | 4000 | 0.1947 | 0.9315 | | 0.1348 | 3.0 | 6000 | 0.1996 | 0.936 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
8daf54bcdc7600a5b2572be935631270
wietsedv/xlm-roberta-base-ft-udpos28-grc
wietsedv
xlm-roberta
8
13
transformers
0
token-classification
true
false
false
apache-2.0
['grc']
['universal_dependencies']
null
0
0
0
0
0
0
0
['part-of-speech', 'token-classification']
true
true
true
575
false
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Ancient Greek This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-grc") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-grc") ```
67a3037a335508188f48a9f953278bc9
Ghost1/bert-finetuned-ner3
Ghost1
bert
12
5
transformers
0
token-classification
true
false
false
apache-2.0
null
['conll2003']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,519
false
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner3 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0603 - Precision: 0.9296 - Recall: 0.9490 - F1: 0.9392 - Accuracy: 0.9863 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0855 | 1.0 | 1756 | 0.0673 | 0.9130 | 0.9340 | 0.9234 | 0.9827 | | 0.0345 | 2.0 | 3512 | 0.0590 | 0.9284 | 0.9445 | 0.9363 | 0.9855 | | 0.0229 | 3.0 | 5268 | 0.0603 | 0.9296 | 0.9490 | 0.9392 | 0.9863 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
4cfec1bb35879750175ab8d6ab9d3c9d
mdroth/distilbert-base-uncased-finetuned-ner
mdroth
distilbert
13
9
transformers
0
token-classification
true
false
false
apache-2.0
null
['conll2003']
null
1
0
1
0
0
0
0
['generated_from_trainer']
true
true
true
1,546
false
<!-- 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-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0635 - Precision: 0.9300 - Recall: 0.9391 - F1: 0.9345 - Accuracy: 0.9841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0886 | 1.0 | 1756 | 0.0676 | 0.9198 | 0.9233 | 0.9215 | 0.9809 | | 0.0382 | 2.0 | 3512 | 0.0605 | 0.9271 | 0.9360 | 0.9315 | 0.9836 | | 0.0247 | 3.0 | 5268 | 0.0635 | 0.9300 | 0.9391 | 0.9345 | 0.9841 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.0 - Datasets 2.0.0 - Tokenizers 0.11.6
91eb11adfe8e9644cf080c0bd7080be7
georgep4181/agp
georgep4181
null
34
26
diffusers
2
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
1
0
1
0
0
0
0
['text-to-image']
false
true
true
2,052
false
### agp Dreambooth model trained by georgep4181 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: agp (use that on your prompt) Keywords: tatto, nychos, nychos art, exploded, grafity ![agp 0](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%281%29.jpg)![agp 1](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%282%29.jpg)![agp 2](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%283%29.jpg)![agp 3](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%284%29.jpg)![agp 4](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%285%29.jpg)![agp 5](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%286%29.jpg)![agp 6](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%287%29.jpg)![agp 7](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%288%29.jpg)![agp 8](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%289%29.jpg)![agp 9](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%2810%29.jpg)![agp 10](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%2811%29.jpg)![agp 11](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%2812%29.jpg)![agp 12](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%2813%29.jpg)![agp 13](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%2814%29.jpg)![agp 14](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%2815%29.jpg)![agp 15](https://huggingface.co/georgep4181/agp/resolve/main/concept_images/agp_%2816%29.jpg)
e94bbca8f35f5730653bb793b9054834
schreon/gpt2-lhm-large-03
schreon
gpt2
11
14
transformers
0
text-generation
true
false
false
mit
null
['training_corpus']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
980
false
<!-- 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-lhm-large-03 This model is a fine-tuned version of [schreon/gpt2-lhm-large-02](https://huggingface.co/schreon/gpt2-lhm-large-02) on the training_corpus 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
9b2ece690c4c022590333e9a7cdc2d4a
sd-concepts-library/mu-sadr
sd-concepts-library
null
14
0
null
0
null
false
false
false
mit
null
null
null
0
0
0
0
0
0
0
[]
false
true
true
1,510
false
### mu-sadr on Stable Diffusion This is the `<783463b>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<783463b> 0](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/3.jpeg) ![<783463b> 1](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/6.jpeg) ![<783463b> 2](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/0.jpeg) ![<783463b> 3](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/7.jpeg) ![<783463b> 4](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/5.jpeg) ![<783463b> 5](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/8.jpeg) ![<783463b> 6](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/1.jpeg) ![<783463b> 7](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/2.jpeg) ![<783463b> 8](https://huggingface.co/sd-concepts-library/mu-sadr/resolve/main/concept_images/4.jpeg)
acef4ee9ac223924cfbe43af08b16e73
muhtasham/small-mlm-glue-cola-custom-tokenizer
muhtasham
bert
12
0
transformers
0
fill-mask
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,918
false
<!-- 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-cola-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.3833 | 0.47 | 500 | 6.0687 | | 5.7664 | 0.94 | 1000 | 5.7078 | | 5.4733 | 1.4 | 1500 | 5.6928 | | 5.2967 | 1.87 | 2000 | 5.6740 | | 5.0316 | 2.34 | 2500 | 5.5465 | | 5.0508 | 2.81 | 3000 | nan | | 5.003 | 3.27 | 3500 | 5.4421 | | 4.7143 | 3.74 | 4000 | 5.3365 | | 4.7795 | 4.21 | 4500 | 5.2115 | | 4.6303 | 4.68 | 5000 | 5.0677 | | 4.5267 | 5.14 | 5500 | 5.2197 | | 4.5582 | 5.61 | 6000 | 5.0485 | | 4.528 | 6.08 | 6500 | 5.1092 | | 4.2633 | 6.55 | 7000 | 5.1459 | | 4.2774 | 7.02 | 7500 | nan | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
0bc851b142606a57dc1abdf7afe8e399
glopez/cifar-10
glopez
vit
7
31
transformers
0
image-classification
true
false
false
apache-2.0
null
['cifar10']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
931
false
<!-- 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. --> # cifar-10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the cifar10 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 2 ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
45248ae0ec8859feb3d414d07812e251
Isaacp/distilbert-base-uncased-finetuned-clinc
Isaacp
distilbert
12
1
transformers
0
text-classification
true
false
false
apache-2.0
null
['clinc_oos']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
933
false
<!-- 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-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
5c4be90ecdbdbedaddc06d0bd89b082c
coreml/coreml-dreamlike-diffusion
coreml
null
6
0
null
4
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
0
0
0
0
1
0
1
['coreml', 'stable-diffusion', 'text-to-image']
false
true
true
5,015
false
# Core ML Converted Model This model was converted to Core ML for use on Apple Silicon devices by following Apple's instructions [here](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).<br> Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br> `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> `original` version is only compatible with CPU & GPU option. Unfortunately, for this model the `split_einsum` version did not generate images correctly and was removed. # Dreamlike Diffusion 1.0 is SD 1.5 fine tuned on high quality art, made by [dreamlike.art](https://dreamlike.art/). Use the same prompts as you would for SD 1.5. Add **dreamlikeart** if the artstyle is too weak. Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a 2:3 or a 9:16 aspect ratio. If you want a landscape photo, try using a 3:2 or a 16:9 aspect ratio. Use slightly higher resolution for better results: 640x640px, 512x768px, 768x512px, etc. ### Examples <img src="https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/preview.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/1.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/2.jpg" style="max-width: 800px;" width="100%"/> ### dreamlike.art You can use this model for free on [dreamlike.art](https://dreamlike.art/)! <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/> ### Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run dreamlike-diffusion-1.0: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/dreamlike-diffusion-1.0) ### CompVis [Download dreamlike-diffusion-1.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/dreamlike-diffusion-1.0.ckpt) ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "dreamlike-art/dreamlike-diffusion-1.0" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "dreamlikeart, a grungy woman with rainbow hair, travelling between dimensions, dynamic pose, happy, soft eyes and narrow chin, extreme bokeh, dainty figure, long hair straight down, torn kawaii shirt and baggy jeans, In style of by Jordan Grimmer and greg rutkowski, crisp lines and color, complex background, particles, lines, wind, concept art, sharp focus, vivid colors" image = pipe(prompt).images[0] image.save("./result.jpg") ``` # License This model is licesed under a **modified** CreativeML OpenRAIL-M license. - **You can't host or use the model or its derivatives on websites/apps/etc., from which you earn, will earn, or plan to earn revenue or donations. If you want to, please email us at contact@dreamlike.art** - **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Diffusion 1.0) and include a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0)** - **You are free to host the model or its derivatives on completely non-commercial websites/apps/etc (Meaning you are not getting ANY revenue or donations). Please state the full model name (Dreamlike Diffusion 1.0) and include a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0)** - **You are free to use the outputs of the model or the outputs of the model's derivatives for commercial purposes in teams of 10 or less** - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/blob/main/LICENSE.md
3b387abcabb6bacc6f2660c14e91ff18
renjithks/distilbert-cord-ner
renjithks
distilbert
12
5
transformers
0
token-classification
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
2,215
false
<!-- 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-cord-ner This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-de-no-da-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-de-no-da-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1670 - Precision: 0.9128 - Recall: 0.9242 - F1: 0.9185 - Accuracy: 0.9656 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 113 | 0.1814 | 0.8480 | 0.8618 | 0.8548 | 0.9393 | | No log | 2.0 | 226 | 0.1755 | 0.8669 | 0.9002 | 0.8832 | 0.9427 | | No log | 3.0 | 339 | 0.1499 | 0.8800 | 0.8935 | 0.8867 | 0.9533 | | No log | 4.0 | 452 | 0.1340 | 0.8975 | 0.9079 | 0.9027 | 0.9596 | | 0.1812 | 5.0 | 565 | 0.1553 | 0.8999 | 0.9146 | 0.9072 | 0.9592 | | 0.1812 | 6.0 | 678 | 0.1474 | 0.8961 | 0.9021 | 0.8991 | 0.9562 | | 0.1812 | 7.0 | 791 | 0.1682 | 0.9135 | 0.9223 | 0.9179 | 0.9622 | | 0.1812 | 8.0 | 904 | 0.1663 | 0.8960 | 0.9175 | 0.9066 | 0.9613 | | 0.0199 | 9.0 | 1017 | 0.1753 | 0.9061 | 0.9261 | 0.9160 | 0.9635 | | 0.0199 | 10.0 | 1130 | 0.1670 | 0.9128 | 0.9242 | 0.9185 | 0.9656 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
8175e147156d535ed5c681b5388bce73
xtlu/ddpm-butterflies-128
xtlu
null
15
2
diffusers
0
null
false
false
false
apache-2.0
['en']
['huggan/smithsonian_butterflies_subset']
null
0
0
0
0
0
0
0
[]
false
true
true
1,226
false
<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/xtlu/ddpm-butterflies-128/tensorboard?#scalars)
95a7ac52f45bd7397ced185f3edac3bf
google/t5-efficient-tiny-nl2
google
t5
12
7
transformers
0
text2text-generation
true
true
true
apache-2.0
['en']
['c4']
null
0
0
0
0
0
0
0
['deep-narrow']
false
true
true
6,244
false
# T5-Efficient-TINY-NL2 (Deep-Narrow version) T5-Efficient-TINY-NL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-tiny-nl2** - is of model type **Tiny** with the following variations: - **nl** is **2** It has **11.9** million parameters and thus requires *ca.* **47.61 MB** of memory in full precision (*fp32*) or **23.81 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.
be1a833c0d6f33587fce12e199a6dc81
Maseshi/Animistatics
Maseshi
null
28
176
diffusers
10
text-to-image
false
false
false
creativeml-openrail-m
['en']
null
null
0
0
0
0
0
0
0
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers']
false
true
true
3,659
false
![Header](https://huggingface.co/Maseshi/Animistatics/resolve/main/assets/header.png) # Animistatics Welcome to Animistatics - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images. e.g. **_girl, cafe, plants, coffee, lighting, steam, blue eyes, brown hair_** ## Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Animistatics: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Maseshi/Animistatics) ## Google Colab We support a [Google Colab](https://github.com/gradio-app/gradio) to run Animistatics: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Qf7KGx7wCQ6XCs4ai_2riq68ip7mZw_t?usp=sharing) ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler import torch repo_id = "Maseshi/Animistatics" pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") prompt = "girl, cafe, plants, coffee, lighting, steam, blue eyes, brown hair" image = pipe(prompt, num_inference_steps=25).images[0] image.save("girl.png") ``` ## Examples Below are some examples of images generated using this model: **Anime Girl:** ![Anime Girl](https://huggingface.co/Maseshi/Animistatics/resolve/main/assets/girl.png) ``` girl, cafe, plants, coffee, lighting, steam, blue eyes, brown hair Steps: 50, Sampler: DDIM, CFG scale: 12 ``` **Anime Boy:** ![Anime Boy](https://huggingface.co/Maseshi/Animistatics/resolve/main/assets/boy.png) ``` boy, blonde hair, blue eyes, colorful, cumulonimbus clouds, lighting, medium hair, plants, city, hoodie, cool Steps: 50, Sampler: DDIM, CFG scale: 12 ``` **City:** ![City](https://huggingface.co/Maseshi/Animistatics/resolve/main/assets/city.png) ``` cityscape, concept art, sun shining through clouds, crepuscular rays, trending on art station, 8k Steps: 50, Sampler: DDIM, CFG scale: 12 ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
235d7d35c97baa83e51e116a8d6a15ff
jonatasgrosman/exp_w2v2t_de_vp-fr_s489
jonatasgrosman
wav2vec2
10
3
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
['de']
['mozilla-foundation/common_voice_7_0']
null
0
0
0
0
0
0
0
['automatic-speech-recognition', 'de']
false
true
true
469
false
# exp_w2v2t_de_vp-fr_s489 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](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.
a21aac3b46716961faf4de33bfe2d96c
Lucetepolis/TriPhaze
Lucetepolis
null
32
0
diffusers
21
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
0
0
0
0
0
0
0
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers']
false
true
true
4,985
false
# TriPhaze ultracolor.v4 - <a href="https://huggingface.co/xdive/ultracolor.v4">Download</a> / <a href="https://arca.live/b/aiart/68609290">Sample</a><br/> Counterfeit-V2.5 - <a href="https://huggingface.co/gsdf/Counterfeit-V2.5">Download / Sample</a><br/> Treebark - <a href="https://huggingface.co/HIZ/aichan_pick">Download</a> / <a href="https://arca.live/b/aiart/67648642">Sample</a><br/> EasyNegative and pastelmix-lora seem to work well with the models. EasyNegative - <a href="https://huggingface.co/datasets/gsdf/EasyNegative">Download / Sample</a><br/> pastelmix-lora - <a href="https://huggingface.co/andite/pastel-mix">Download / Sample</a> # Formula ``` ultracolor.v4 + Counterfeit-V2.5 = temp1 U-Net Merge - 0.870333, 0.980430, 0.973645, 0.716758, 0.283242, 0.026355, 0.019570, 0.129667, 0.273791, 0.424427, 0.575573, 0.726209, 0.5, 0.726209, 0.575573, 0.424427, 0.273791, 0.129667, 0.019570, 0.026355, 0.283242, 0.716758, 0.973645, 0.980430, 0.870333 temp1 + Treebark = temp2 U-Net Merge - 0.752940, 0.580394, 0.430964, 0.344691, 0.344691, 0.430964, 0.580394, 0.752940, 0.902369, 0.988642, 0.988642, 0.902369, 0.666667, 0.902369, 0.988642, 0.988642, 0.902369, 0.752940, 0.580394, 0.430964, 0.344691, 0.344691, 0.430964, 0.580394, 0.752940 temp2 + ultracolor.v4 = TriPhaze_A U-Net Merge - 0.042235, 0.056314, 0.075085, 0.100113, 0.133484, 0.177979, 0.237305, 0.316406, 0.421875, 0.5625, 0.75, 1, 0.5, 1, 0.75, 0.5625, 0.421875, 0.316406, 0.237305, 0.177979, 0.133484, 0.100113, 0.075085, 0.056314, 0.042235 ultracolor.v4 + Counterfeit-V2.5 = temp3 U-Net Merge - 0.979382, 0.628298, 0.534012, 0.507426, 0.511182, 0.533272, 0.56898, 0.616385, 0.674862, 0.7445, 0.825839, 0.919748, 0.5, 0.919748, 0.825839, 0.7445, 0.674862, 0.616385, 0.56898, 0.533272, 0.511182, 0.507426, 0.534012, 0.628298, 0.979382 temp3 + Treebark = TriPhaze_C U-Net Merge - 0.243336, 0.427461, 0.566781, 0.672199, 0.751965, 0.812321, 0.857991, 0.892547, 0.918694, 0.938479, 0.953449, 0.964777, 0.666667, 0.964777, 0.953449, 0.938479, 0.918694, 0.892547, 0.857991, 0.812321, 0.751965, 0.672199, 0.566781, 0.427461, 0.243336 TriPhaze_A + TriPhaze_C = TriPhaze_B U-Net Merge - 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 ![A](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Graphs/A.png) ![B](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Graphs/B.png) ![C](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Graphs/C.png) # 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, nsfw Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 1853114200, Size: 768x512, Model hash: 6bad0b419f, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B ``` # TriPhaze_A ![A01](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A01.png) ![A02](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A02.png) ![A03](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A03.png) ![A04](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A04.png) ![A05](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A05.png) ![A06](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A06.png) ![A07](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A07.png) ![A08](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/A08.png) # TriPhaze_B ![B01](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B01.png) ![B02](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B02.png) ![B03](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B03.png) ![B04](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B04.png) ![B05](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B05.png) ![B06](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B06.png) ![B07](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B07.png) ![B08](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/B08.png) # TriPhaze_C ![C01](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C01.png) ![C02](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C02.png) ![C03](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C03.png) ![C04](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C04.png) ![C05](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C05.png) ![C06](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C06.png) ![C07](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C07.png) ![C08](https://huggingface.co/Lucetepolis/TriPhaze/resolve/main/Samples/C08.png)
dc3bebe3528bd4f11ec76d73983d9d3c
Helsinki-NLP/opus-mt-lua-sv
Helsinki-NLP
marian
10
10
transformers
0
translation
true
true
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['translation']
false
true
true
776
false
### opus-mt-lua-sv * source languages: lua * target languages: sv * OPUS readme: [lua-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lua-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lua-sv/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-sv/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-sv/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lua.sv | 25.7 | 0.437 |
a45cee3cb2405ce17cb45e5e48d99da1
DOOGLAK/Article_100v3_NER_Model_3Epochs_UNAUGMENTED
DOOGLAK
bert
13
5
transformers
0
token-classification
true
false
false
apache-2.0
null
['article100v3_wikigold_split']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,537
false
<!-- 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. --> # Article_100v3_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6272 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7772 ## 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 | 11 | 0.7637 | 0.0 | 0.0 | 0.0 | 0.7772 | | No log | 2.0 | 22 | 0.6651 | 0.0 | 0.0 | 0.0 | 0.7772 | | No log | 3.0 | 33 | 0.6272 | 0.0 | 0.0 | 0.0 | 0.7772 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6
f51f0d0f1709abd9639d1a3de3b94341
CavenLen/ddpm-Kaga-128
CavenLen
null
116
3
diffusers
0
null
false
false
false
apache-2.0
['en']
['CavenLen/Kaga']
null
0
0
0
0
0
0
0
[]
false
true
true
1,192
false
<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-Kaga-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `CavenLen/Kaga` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/CavenLen/ddpm-Kaga-128/tensorboard?#scalars)
0d4815d0110481d502557599e3df8a5c
Helsinki-NLP/opus-mt-ms-de
Helsinki-NLP
marian
11
7
transformers
0
translation
true
true
false
apache-2.0
['ms', 'de']
null
null
1
1
0
0
0
0
0
['translation']
false
true
true
2,063
false
### msa-deu * source group: Malay (macrolanguage) * target group: German * OPUS readme: [msa-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/msa-deu/README.md) * model: transformer-align * source language(s): ind zsm_Latn * target language(s): deu * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/msa-deu/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/msa-deu/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/msa-deu/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.msa.deu | 36.5 | 0.584 | ### System Info: - hf_name: msa-deu - source_languages: msa - target_languages: deu - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/msa-deu/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ms', 'de'] - src_constituents: {'zsm_Latn', 'ind', 'max_Latn', 'zlm_Latn', 'min'} - tgt_constituents: {'deu'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/msa-deu/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/msa-deu/opus-2020-06-17.test.txt - src_alpha3: msa - tgt_alpha3: deu - short_pair: ms-de - chrF2_score: 0.584 - bleu: 36.5 - brevity_penalty: 0.966 - ref_len: 4198.0 - src_name: Malay (macrolanguage) - tgt_name: German - train_date: 2020-06-17 - src_alpha2: ms - tgt_alpha2: de - prefer_old: False - long_pair: msa-deu - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
6db15e547a10193ee2ab8414a6df8708
Alireza1044/albert-base-v2-mnli
Alireza1044
albert
14
10
transformers
0
text-classification
true
false
false
apache-2.0
['en']
['glue']
null
1
1
0
0
0
0
0
['generated_from_trainer']
false
true
true
994
false
<!-- 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. --> # mnli This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5383 - Accuracy: 0.8501 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Training results ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
b3b1bec91f5cc27cf91b8e0aaa1e5e35
jxuhf/Fine-tuning-text-classification-model-Habana-Gaudi
jxuhf
bert
14
1
transformers
0
text-classification
true
false
false
apache-2.0
['en']
['glue']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,086
false
<!-- 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. --> # mrpc This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.3680 - Accuracy: 0.8824 - F1: 0.9181 - Combined Score: 0.9002 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0a0+gitfe03f8c - Datasets 2.1.0 - Tokenizers 0.12.1
0b9a440b68c08108ad846ed426986793
sd-concepts-library/tesla-bot
sd-concepts-library
null
12
0
null
1
null
false
false
false
mit
null
null
null
0
0
0
0
1
1
0
[]
false
true
true
1,338
false
### Tesla Bot on Stable Diffusion This is the `<tesla-bot>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<tesla-bot> 0](https://huggingface.co/sd-concepts-library/tesla-bot/resolve/main/concept_images/1.jpeg) ![<tesla-bot> 1](https://huggingface.co/sd-concepts-library/tesla-bot/resolve/main/concept_images/2.jpeg) ![<tesla-bot> 2](https://huggingface.co/sd-concepts-library/tesla-bot/resolve/main/concept_images/0.jpeg) ![<tesla-bot> 3](https://huggingface.co/sd-concepts-library/tesla-bot/resolve/main/concept_images/3.jpeg) ![<tesla-bot> 4](https://huggingface.co/sd-concepts-library/tesla-bot/resolve/main/concept_images/4.jpeg) ![<tesla-bot> 5](https://huggingface.co/sd-concepts-library/tesla-bot/resolve/main/concept_images/5.jpeg) ![<tesla-bot> 6](https://huggingface.co/sd-concepts-library/tesla-bot/resolve/main/concept_images/6.jpeg)
ec08bd4e2b6715cab96f3a0696b46e30
Harsit/bert-finetuned-squad
Harsit
bert
12
3
transformers
1
question-answering
true
false
false
apache-2.0
null
['squad']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
954
false
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
f47240a32efd469cca77e554184c5312
itaihay/wav2vec_asr_swbd_10_epochs
itaihay
wav2vec2
16
16
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
4,051
false
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec_asr_swbd_10_epochs This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 0.9627 ## 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: 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_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:------:|:---------------:|:------:| | 1.0682 | 0.22 | 5000 | 0.7383 | 0.4431 | | 0.9143 | 0.44 | 10000 | 0.7182 | 0.4058 | | 0.8905 | 0.66 | 15000 | 0.6291 | 0.3987 | | 0.8354 | 0.87 | 20000 | 0.5976 | 0.3954 | | 0.7749 | 1.09 | 25000 | 0.5773 | 0.3901 | | 0.7336 | 1.31 | 30000 | 0.5812 | 0.3871 | | 0.7314 | 1.53 | 35000 | 0.5802 | 0.3895 | | 0.0 | 1.75 | 40000 | nan | 0.9627 | | 0.0 | 1.97 | 45000 | nan | 0.9627 | | 0.0 | 2.19 | 50000 | nan | 0.9627 | | 0.0 | 2.4 | 55000 | nan | 0.9627 | | 0.0 | 2.62 | 60000 | nan | 0.9627 | | 0.0 | 2.84 | 65000 | nan | 0.9627 | | 0.0 | 3.06 | 70000 | nan | 0.9627 | | 0.0 | 3.28 | 75000 | nan | 0.9627 | | 0.0 | 3.5 | 80000 | nan | 0.9627 | | 0.0 | 3.72 | 85000 | nan | 0.9627 | | 0.0 | 3.93 | 90000 | nan | 0.9627 | | 0.0 | 4.15 | 95000 | nan | 0.9627 | | 0.0 | 4.37 | 100000 | nan | 0.9627 | | 0.0 | 4.59 | 105000 | nan | 0.9627 | | 0.0 | 4.81 | 110000 | nan | 0.9627 | | 0.0 | 5.03 | 115000 | nan | 0.9627 | | 0.0 | 5.25 | 120000 | nan | 0.9627 | | 0.0 | 5.46 | 125000 | nan | 0.9627 | | 0.0 | 5.68 | 130000 | nan | 0.9627 | | 0.0 | 5.9 | 135000 | nan | 0.9627 | | 0.0 | 6.12 | 140000 | nan | 0.9627 | | 0.0 | 6.34 | 145000 | nan | 0.9627 | | 0.0 | 6.56 | 150000 | nan | 0.9627 | | 0.0 | 6.78 | 155000 | nan | 0.9627 | | 0.0 | 7.0 | 160000 | nan | 0.9627 | | 0.0 | 7.21 | 165000 | nan | 0.9627 | | 0.0 | 7.43 | 170000 | nan | 0.9627 | | 0.0 | 7.65 | 175000 | nan | 0.9627 | | 0.0 | 7.87 | 180000 | nan | 0.9627 | | 0.0 | 8.09 | 185000 | nan | 0.9627 | | 0.0 | 8.31 | 190000 | nan | 0.9627 | | 0.0 | 8.53 | 195000 | nan | 0.9627 | | 0.0 | 8.74 | 200000 | nan | 0.9627 | | 0.0 | 8.96 | 205000 | nan | 0.9627 | | 0.0 | 9.18 | 210000 | nan | 0.9627 | | 0.0 | 9.4 | 215000 | nan | 0.9627 | | 0.0 | 9.62 | 220000 | nan | 0.9627 | | 0.0 | 9.84 | 225000 | nan | 0.9627 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
b3f12e7312845e90d2e587f5d53de21c
Chandanab/swin-tiny-patch4-window7-224-finetuned-eurosat
Chandanab
swin
11
4
transformers
0
image-classification
true
false
false
apache-2.0
null
['image_folder']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,491
false
<!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1677 - Accuracy: 0.9394 ## 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: 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.3554 | 0.8081 | | 0.4819 | 2.0 | 14 | 0.2077 | 0.9091 | | 0.1985 | 3.0 | 21 | 0.1677 | 0.9394 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.2.0 - Tokenizers 0.12.1
04c41d9277cb0a89154c5551cbf954a8
tftransformers/t5-small
tftransformers
null
6
6
null
0
translation
false
false
false
apache-2.0
['en', 'fr', 'ro', 'de']
['c4']
null
0
0
0
0
0
0
0
['summarization', 'translation']
false
true
true
1,918
false
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Other Community Checkpoints: [here](https://huggingface.co/models?search=t5) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67) ## Usage ``` from tf_transformers.models import T5Model # Any T5 model (t5-small, t5-base, t5-large etc) model_name = 't5-small' model = T5Model.from_pretrained(model_name) ```
7691059cd16936e462eab7e94ca5b74b
microsoft/tapex-base-finetuned-wikisql
microsoft
bart
12
209
transformers
3
table-question-answering
true
false
false
mit
['en']
['wikisql']
null
0
0
0
0
0
0
0
['tapex', 'table-question-answering']
false
true
true
3,054
false
# TAPEX (base-sized model) TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining). ## Model description TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. This model is the `tapex-base` model fine-tuned on the [WikiSQL](https://huggingface.co/datasets/wikisql) dataset. ## Intended Uses You can use the model for table question answering on relatively simple questions. Some **solveable** questions are shown below (corresponding tables now shown): | Question | Answer | |:---: |:---:| | tell me what the notes are for south australia | no slogan on current series | | what position does the player who played for butler cc (ks) play? | guard-forward | | how many schools did player number 3 play at? | 1.0 | | how many winning drivers in the kraco twin 125 (r2) race were there? | 1.0 | | for the episode(s) aired in the u.s. on 4 april 2008, what were the names? | "bust a move" part one, "bust a move" part two | ### How to Use Here is how to use this model in transformers: ```python from transformers import TapexTokenizer, BartForConditionalGeneration import pandas as pd tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base-finetuned-wikisql") model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base-finetuned-wikisql") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) # tapex accepts uncased input since it is pre-trained on the uncased corpus query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008.0'] ``` ### How to Eval Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex). ### BibTeX entry and citation info ```bibtex @inproceedings{ liu2022tapex, title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor}, author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=O50443AsCP} } ```
2041f81217becd72f8509293d6f0d9c9
amanm27/bert-base-uncased-sports
amanm27
bert
9
9
transformers
0
fill-mask
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,246
false
<!-- 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-sports 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: 2.0064 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.4926 | 1.0 | 912 | 2.1186 | | 2.2168 | 2.0 | 1824 | 2.0392 | | 2.1327 | 3.0 | 2736 | 2.0081 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0 - Datasets 1.18.3 - Tokenizers 0.11.0
5acc9d65716dea7cc86f1e9ae10cee82
KarelDO/roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_42
KarelDO
roberta
15
2
transformers
0
null
true
false
false
mit
['en']
['OpenTable']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,123
false
<!-- 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.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_42 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.6579 - Accuracy: 0.7352 - Macro-f1: 0.7190 - Weighted-macro-f1: 0.7313 ## 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: 5.0 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.5.2 - Tokenizers 0.12.1
847133ab849a8b5c6f4f5eb840419e53
chisun/mt5-small-finetuned-amazon-en-es
chisun
mt5
12
3
transformers
0
summarization
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['summarization', 'generated_from_trainer']
true
true
true
1,998
false
<!-- 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-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6642 - Rouge1: 12.9097 - Rouge2: 3.2756 - Rougel: 12.2885 - Rougelsum: 12.3186 ## 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: 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | No log | 1.0 | 300 | 5.0305 | 4.4679 | 0.4134 | 4.3487 | 4.2807 | | 9.3723 | 2.0 | 600 | 3.8535 | 10.6408 | 2.6198 | 10.5538 | 10.5819 | | 9.3723 | 3.0 | 900 | 3.7590 | 12.1502 | 3.3079 | 12.013 | 12.1208 | | 4.3429 | 4.0 | 1200 | 3.7019 | 13.0029 | 3.7708 | 12.9759 | 12.876 | | 4.3429 | 5.0 | 1500 | 3.6782 | 13.1362 | 3.0904 | 12.561 | 12.5702 | | 4.0043 | 6.0 | 1800 | 3.6698 | 12.8674 | 3.8026 | 12.3664 | 12.4216 | | 4.0043 | 7.0 | 2100 | 3.6644 | 12.9581 | 3.3843 | 12.407 | 12.3956 | | 3.872 | 8.0 | 2400 | 3.6642 | 12.9097 | 3.2756 | 12.2885 | 12.3186 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu116 - Datasets 2.4.0 - Tokenizers 0.12.1
d8bde60f9ca05ef08ceb6e4bd8663fd3
muhtasham/small-mlm-glue-rte-target-glue-sst2
muhtasham
bert
10
3
transformers
0
text-classification
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,811
false
<!-- 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-sst2 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: 0.4047 - Accuracy: 0.8853 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.39 | 0.24 | 500 | 0.3545 | 0.8452 | | 0.3071 | 0.48 | 1000 | 0.3333 | 0.8486 | | 0.2584 | 0.71 | 1500 | 0.3392 | 0.8716 | | 0.2388 | 0.95 | 2000 | 0.3082 | 0.8807 | | 0.1863 | 1.19 | 2500 | 0.3273 | 0.8865 | | 0.1691 | 1.43 | 3000 | 0.3945 | 0.8704 | | 0.1675 | 1.66 | 3500 | 0.3601 | 0.8853 | | 0.1713 | 1.9 | 4000 | 0.3341 | 0.8899 | | 0.1368 | 2.14 | 4500 | 0.4622 | 0.8532 | | 0.1251 | 2.38 | 5000 | 0.4047 | 0.8853 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
532e849193a5b9248745b134f283b308
darkstorm2150/Protogen_x5.8_Official_Release
darkstorm2150
null
23
18,179
diffusers
73
text-to-image
false
false
false
creativeml-openrail-m
['en']
null
null
11
6
2
3
1
1
0
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers']
false
true
true
3,552
false
# Protogen_v5.8 by [darkstorm2150](https://instagram.com/officialvictorespinoza) Protogen was warm-started with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and is rebuilt using dreamlikePhotoRealV2.ckpt as a core, adding small amounts during merge checkpoints. ## Model Weights ![alt text](https://huggingface.co/darkstorm2150/Protogen_x5.8_Official_Release/resolve/main/Model%20Weights.png) ## Space We support a [Gradio](https://github.com/gradio-app/gradio) Web UI: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/darkstorm2150/Stable-Diffusion-Protogen-webui) ### CompVis [Download ProtoGen_X5.8.ckpt) (7.2GB)](https://huggingface.co/darkstorm2150/Protogen_v5.8_Official_Release/blob/main/ProtoGen_X5.8.ckpt) ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler import torch prompt = ( "modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot body photo of the most beautiful artwork in the world, " "english medieval witch, black silk vale, pale skin, black silk robe, black cat, necromancy magic, medieval era, " "photorealistic painting by Ed Blinkey, Atey Ghailan, Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation, " "trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, photorealistic painting art by midjourney and greg rutkowski" ) model_id = "darkstorm2150/Protogen_v5.8_Official_Release" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") image = pipe(prompt, num_inference_steps=25).images[0] image.save("./result.jpg") ``` License This model is licesed under a modified CreativeML OpenRAIL-M license. You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at contact@dreamlike.art You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Photoreal 2.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0) You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less You can't use the model to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the modified CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md
0151ac25bcaf80f8f8504f4e6466cf2e
SetFit/distilbert-base-uncased__sst2__train-8-1
SetFit
distilbert
10
5
transformers
0
text-classification
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,888
false
<!-- 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__sst2__train-8-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6930 - Accuracy: 0.5047 ## 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 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7082 | 1.0 | 3 | 0.7048 | 0.25 | | 0.6761 | 2.0 | 6 | 0.7249 | 0.25 | | 0.6653 | 3.0 | 9 | 0.7423 | 0.25 | | 0.6212 | 4.0 | 12 | 0.7727 | 0.25 | | 0.5932 | 5.0 | 15 | 0.8098 | 0.25 | | 0.5427 | 6.0 | 18 | 0.8496 | 0.25 | | 0.5146 | 7.0 | 21 | 0.8992 | 0.25 | | 0.4356 | 8.0 | 24 | 0.9494 | 0.25 | | 0.4275 | 9.0 | 27 | 0.9694 | 0.25 | | 0.3351 | 10.0 | 30 | 0.9968 | 0.25 | | 0.2812 | 11.0 | 33 | 1.0056 | 0.5 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3
d813d6e2cce202e2117663524dbc71c8
nvidia/stt_eo_conformer_ctc_large
nvidia
null
3
4
nemo
0
automatic-speech-recognition
true
false
false
cc-by-4.0
['eo']
['mozilla-foundation/common_voice_11_0']
null
0
0
0
0
0
0
0
['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva']
true
true
true
5,889
false
# NVIDIA Conformer-CTC Large (Esperanto) <style> img { display: inline; } </style> | [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--CTC-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-120M-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-eo-lightgrey#model-badge)](#datasets) | [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) | This model transcribes speech into lowercase Esperanto alphabet including spaces and apostroph. The model was obtained by finetuning from English SSL-pretrained model on Mozilla Common Voice Esperanto 11.0 dataset. It is a non-autoregressive "large" variant of Conformer [1], with around 120 million parameters. See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details. It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). ## Usage The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for finetuning on another dataset. To train, finetune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version. ``` pip install nemo_toolkit['all'] ``` ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_eo_conformer_ctc_large") ``` ### Transcribing using Python Simply do: ``` asr_model.transcribe(['<your_audio>.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_eo_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16 kHz mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc). ## Training The NeMo toolkit [3] was used for finetuning from English SSL model for over several hundred epochs. The model is finetuning with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml). As pretrained English SSL model we use [ssl_en_conformer_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/ssl_en_conformer_large) which was trained using LibriLight corpus (~56k hrs of unlabeled English speech). The tokenizer for the model was built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). Full config can be found inside the .nemo files. More training details can be found at the [Esperanto ASR example](https://github.com/andrusenkoau/NeMo/blob/esperanto_example/docs/source/asr/examples/esperanto_asr/esperanto_asr.rst). ### Datasets All the models were trained on Mozilla Common Voice Esperanto 11.0 dataset comprising of about 1400 validated hours of Esperanto speech. However, training set consists of a much smaller amount of data, because when forming the train.tsv, dev.tsv and test.tsv, repetitions of texts in train were removed by Mozilla developers. - Train set: ~250 hours. - Dev set: ~25 hours. - Test: ~25 hours. ## Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | Dev WER| Test WER| Train Dataset | |---------|-----------------------|-----------------|--------|---------|-----------------| | 1.14.0 | SentencePiece [2] BPE | 128 | 2.9 | 4.8 | MCV-11.0 Train set | ## Limitations Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## Deployment with NVIDIA Riva For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References - [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100) - [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
c084d671bc4087675caa5f68fa2704f9
sd-concepts-library/moeb-style
sd-concepts-library
null
9
0
null
21
null
false
false
false
mit
null
null
null
0
0
0
0
0
0
0
[]
false
true
true
1,018
false
### Moeb Style on Stable Diffusion This is the `<moe-bius>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<moe-bius> 0](https://huggingface.co/sd-concepts-library/moeb-style/resolve/main/concept_images/1.jpeg) ![<moe-bius> 1](https://huggingface.co/sd-concepts-library/moeb-style/resolve/main/concept_images/3.jpeg) ![<moe-bius> 2](https://huggingface.co/sd-concepts-library/moeb-style/resolve/main/concept_images/2.jpeg) ![<moe-bius> 3](https://huggingface.co/sd-concepts-library/moeb-style/resolve/main/concept_images/0.jpeg)
7a524bdc7258466f76df8c1e3372ad00
toasthans/Facebook_and_Twitter_Ohne_HPS
toasthans
bert
12
5
transformers
0
text-classification
true
false
false
mit
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,405
false
<!-- 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. --> # Facebook_and_Twitter_Ohne_HPS This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9218 - Accuracy: 0.8512 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4364 | 1.0 | 713 | 0.4107 | 0.8302 | | 0.2843 | 2.0 | 1426 | 0.4316 | 0.8495 | | 0.0869 | 3.0 | 2139 | 0.7700 | 0.8558 | | 0.0443 | 4.0 | 2852 | 0.9218 | 0.8512 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
aa7cfda57d8a1bc17e38143d2cd7ec58
ashish23993/t5-small-finetuned-xsum-AB
ashish23993
t5
9
4
transformers
0
text2text-generation
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,372
false
<!-- 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-xsum-AB 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: 2.8942 - Rouge1: 13.835 - Rouge2: 4.4916 - Rougel: 10.5998 - Rougelsum: 12.3225 - Gen Len: 19.0 ## 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 2.9182 | 1.0 | 625 | 2.8942 | 13.835 | 4.4916 | 10.5998 | 12.3225 | 19.0 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.13.0+cpu - Datasets 2.6.1 - Tokenizers 0.13.1
9730088492af790e0f8d346df46cc693
lewtun/roberta-large-finetuned-clinc-12
lewtun
roberta
15
2
transformers
0
text-classification
true
false
false
mit
null
['clinc_oos']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,403
false
<!-- 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-large-finetuned-clinc-12 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1429 - Accuracy: 0.9765 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8662 | 1.0 | 954 | 0.3441 | 0.9339 | | 0.158 | 2.0 | 1908 | 0.1498 | 0.9742 | | 0.0469 | 3.0 | 2862 | 0.1429 | 0.9765 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
ac97eb3bb4836ff05fc5f5e7a5222477
tzvc/3647bbc5-4fbe-4a94-95ec-5aec23a04e73
tzvc
null
25
2
diffusers
0
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
1
1
0
0
0
0
0
['text-to-image']
false
true
true
784
false
### training params ```json { "pretrained_model_name_or_path": "multimodalart/sd-fine-tunable", "instance_data_dir": "./3647bbc5-4fbe-4a94-95ec-5aec23a04e73/instance_data", "class_data_dir": "./class_data/person", "output_dir": "./3647bbc5-4fbe-4a94-95ec-5aec23a04e73/", "train_text_encoder": true, "with_prior_preservation": false, "prior_loss_weight": 1.0, "instance_prompt": "sd-tzvc", "class_prompt": "person", "resolution": 512, "train_batch_size": 1, "gradient_accumulation_steps": 1, "gradient_checkpointing": true, "use_8bit_adam": true, "learning_rate": 2e-06, "lr_scheduler": "polynomial", "lr_warmup_steps": 0, "num_class_images": 500, "max_train_steps": 1050, "mixed_precision": "fp16" } ```
d19316664557beb76ca272155a9d8bde
premsuresh/bart-finetuned-mathqa-moh
premsuresh
bart
8
6
transformers
0
text2text-generation
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
960
false
<!-- 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. --> # bart-finetuned-mathqa-moh This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
c43b70a08a070c31e611a4413d75c257
willcai/wav2vec2_common_voice_accents_5
willcai
wav2vec2
11
6
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
null
['common_voice']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
2,436
false
<!-- 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_common_voice_accents_5 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: 0.0027 ## 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: 48 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 384 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.4163 | 1.34 | 400 | 0.5520 | | 0.3305 | 2.68 | 800 | 0.1698 | | 0.2138 | 4.03 | 1200 | 0.1104 | | 0.1714 | 5.37 | 1600 | 0.0944 | | 0.1546 | 6.71 | 2000 | 0.0700 | | 0.1434 | 8.05 | 2400 | 0.0610 | | 0.1272 | 9.4 | 2800 | 0.0493 | | 0.1183 | 10.74 | 3200 | 0.0371 | | 0.1113 | 12.08 | 3600 | 0.0468 | | 0.1013 | 13.42 | 4000 | 0.0336 | | 0.0923 | 14.77 | 4400 | 0.0282 | | 0.0854 | 16.11 | 4800 | 0.0410 | | 0.0791 | 17.45 | 5200 | 0.0252 | | 0.0713 | 18.79 | 5600 | 0.0128 | | 0.0662 | 20.13 | 6000 | 0.0252 | | 0.0635 | 21.48 | 6400 | 0.0080 | | 0.0607 | 22.82 | 6800 | 0.0098 | | 0.0557 | 24.16 | 7200 | 0.0069 | | 0.0511 | 25.5 | 7600 | 0.0057 | | 0.0474 | 26.85 | 8000 | 0.0046 | | 0.045 | 28.19 | 8400 | 0.0037 | | 0.0426 | 29.53 | 8800 | 0.0027 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6
4c7b82773b79ba94c4b08638d9f9aef3
jimregan/bert-base-irish-cased-v1-finetuned-ner
jimregan
bert
16
8
transformers
0
token-classification
true
false
false
apache-2.0
['ga']
['wikiann']
null
0
0
0
0
0
0
0
['generated_from_trainer', 'irish']
true
true
true
1,757
false
<!-- 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-irish-cased-v1-finetuned-ner This model is a fine-tuned version of [DCU-NLP/bert-base-irish-cased-v1](https://huggingface.co/DCU-NLP/bert-base-irish-cased-v1) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2468 - Precision: 0.8191 - Recall: 0.8363 - F1: 0.8276 - Accuracy: 0.9307 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 63 | 0.4902 | 0.5579 | 0.5269 | 0.5420 | 0.8458 | | No log | 2.0 | 126 | 0.3227 | 0.7169 | 0.7417 | 0.7291 | 0.8991 | | No log | 3.0 | 189 | 0.2720 | 0.7895 | 0.7839 | 0.7867 | 0.9186 | | No log | 4.0 | 252 | 0.2585 | 0.8128 | 0.8296 | 0.8211 | 0.9264 | | No log | 5.0 | 315 | 0.2468 | 0.8191 | 0.8363 | 0.8276 | 0.9307 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
13d8958f90e9e5b3d560669be4d198fa
Ashenhard/Ashenhard-style
Ashenhard
null
13
0
null
2
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
0
0
0
0
0
0
0
['text-to-image']
false
true
true
1,400
false
I'm a digital artist learning these new tools to work with, this is my first style model I'm on Instagram: @ashenhard84 and Twitter: @ashenhard This model was trained with 85 images, at 8500 steps 1e-6 in Shivam Shrirao Google colab. I think the potential of this model is to merge it with others. The token is **Ashenhard style** **Generated by the model without merge:** ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/modelalone1.png) ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/modelalone2.png) ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/modelalone3.png) ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/modelalone4.png) **Generated by the model merged with (A) Anything V3 at 0.4 - (B) Ashenhard:** ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/modelanything1.png) ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/modelanything2.png) **Testing Img2Img with the model+anything** ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/modelanythingimg2img.png) **Generated by the model merged with (A) Ashenhard at 0.4 - (B) F222:** ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/ModelF222.png) ![Samples](https://huggingface.co/Ashenhard/Ashenhard-style/resolve/main/ModelF222-2.png)
2b86852ce24d083b9241897435960290
Nadav/bert-base-french-europeana-cased-squad-fr
Nadav
bert
10
23
transformers
0
question-answering
true
false
false
mit
null
null
null
1
1
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,292
false
<!-- 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-french-europeana-cased-squad-fr This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7031 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9069 | 1.0 | 3539 | 1.7853 | | 1.6263 | 2.0 | 7078 | 1.7031 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
f4609471e284cdc8cd2ec63e90b71575
elopezlopez/distilbert-base-uncased_fold_2_ternary
elopezlopez
distilbert
13
1
transformers
0
text-classification
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
2,656
false
<!-- 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_fold_2_ternary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5810 - F1: 0.7620 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 294 | 0.5886 | 0.7239 | | 0.557 | 2.0 | 588 | 0.5085 | 0.7524 | | 0.557 | 3.0 | 882 | 0.6332 | 0.7530 | | 0.2456 | 4.0 | 1176 | 0.8749 | 0.7161 | | 0.2456 | 5.0 | 1470 | 1.0601 | 0.7371 | | 0.1112 | 6.0 | 1764 | 1.1885 | 0.7451 | | 0.0484 | 7.0 | 2058 | 1.3027 | 0.7240 | | 0.0484 | 8.0 | 2352 | 1.4647 | 0.7259 | | 0.0259 | 9.0 | 2646 | 1.4476 | 0.7322 | | 0.0259 | 10.0 | 2940 | 1.4826 | 0.7388 | | 0.0164 | 11.0 | 3234 | 1.5869 | 0.7333 | | 0.0109 | 12.0 | 3528 | 1.5954 | 0.7539 | | 0.0109 | 13.0 | 3822 | 1.5810 | 0.7620 | | 0.0082 | 14.0 | 4116 | 1.7165 | 0.7335 | | 0.0082 | 15.0 | 4410 | 1.8152 | 0.7414 | | 0.004 | 16.0 | 4704 | 1.7411 | 0.7474 | | 0.004 | 17.0 | 4998 | 1.8692 | 0.7355 | | 0.0034 | 18.0 | 5292 | 1.8727 | 0.7303 | | 0.0009 | 19.0 | 5586 | 1.9813 | 0.7305 | | 0.0009 | 20.0 | 5880 | 1.9764 | 0.7391 | | 0.0012 | 21.0 | 6174 | 2.0170 | 0.7291 | | 0.0012 | 22.0 | 6468 | 2.0240 | 0.7391 | | 0.0004 | 23.0 | 6762 | 2.0311 | 0.7352 | | 0.0014 | 24.0 | 7056 | 2.0174 | 0.7334 | | 0.0014 | 25.0 | 7350 | 2.0282 | 0.7381 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
aea03357e491ac999c6e8dda7f882d20
Den4ikAI/rubert_large_squad_2
Den4ikAI
bert
11
3
transformers
0
question-answering
true
false
false
mit
null
null
null
0
0
0
0
0
0
0
[]
false
true
true
502
false
обученный rubert от sberbank-ai/ruBert-base. размер выборки - 4. Эпохи - 4. ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="Den4ikAI/rubert_large_squad_2", tokenizer="Den4ikAI/rubert_large_squad_2" ) predictions = qa_pipeline({ 'context': "Пушкин родился 6 июля 1799 года", 'question': "Когда родился Пушкин?" }) print(predictions) # output: #{'score': 0.8013797664642334, 'start': 15, 'end': 31, 'answer': '6 июля 1799 года'} ```
14eef434cecb6883b306dc777a051ec9
Pablo94/racism-finetuned-detests
Pablo94
roberta
13
0
transformers
0
text-classification
true
false
false
cc
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,760
false
<!-- 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. --> # racism-finetuned-detests This model is a fine-tuned version of [davidmasip/racism](https://huggingface.co/davidmasip/racism) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0150 - Accuracy: 0.8560 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2659 | 1.0 | 153 | 0.3250 | 0.8429 | | 0.1191 | 2.0 | 306 | 0.5344 | 0.8380 | | 0.0074 | 3.0 | 459 | 0.8188 | 0.8396 | | 0.0001 | 4.0 | 612 | 0.9264 | 0.8462 | | 0.0001 | 5.0 | 765 | 0.9551 | 0.8462 | | 0.0001 | 6.0 | 918 | 0.9771 | 0.8527 | | 0.0001 | 7.0 | 1071 | 0.9937 | 0.8527 | | 0.0001 | 8.0 | 1224 | 1.0054 | 0.8560 | | 0.0 | 9.0 | 1377 | 1.0126 | 0.8560 | | 0.0001 | 10.0 | 1530 | 1.0150 | 0.8560 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
92c1f779ecf63012f2150153e8c67132
N75242/FloralMarbles_Model
N75242
null
25
0
null
7
null
false
false
false
creativeml-openrail-m
null
null
null
0
0
0
0
0
0
0
[]
false
true
true
3,053
false
### Model info --- This is a dreambooth model trained with the data set of [FloralMarble](https://huggingface.co/datasets/spaablauw/FloralMarble_dataset) on top of stable diffusion 1.5, all creadits to [spaablauw](https://huggingface.co/spaablauw) for original images. I left several models uploaded, all the intermediate steps + two anime models that I merged into. I would recomend try [the 4000 steps model](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/FloralMarble_step_4000.ckpt) or the [7000 steps one](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/FloralMarble_step_7000.ckpt), it depends a bit in what you want, I had relly good result in booth. For img2img 7000 step version is better. [Download Eimis Merge](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/EimisAnimeDiffusion_1-0v_0-FloralMarble_step_3000.safetensors) [Download Anything Merge](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/Anything-V3.0_0-FloralMarble_step_3000_1.safetensors) Use whatever VAE you want. --- ### Examples, download images to get prompts from exif data ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0002-3659297088.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0004-3659297088.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0012-2092274985.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0013-2092274985.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0023-774684095.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0046-4269222975.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0055-2404365075.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/xy_grid-0003-3279396972.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/xy_grid-0004-1720742584.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/xy_grid-0006-1034387134.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0072-2870034878.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0071-2870034878.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0069-2870034878.png) ![comparison_image](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/grid-0004-1540360593.png) --- ### Tag list [Get the tag list images had here](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/tags.txt) I used "flrmrbl" as an unique token, so it should activate the model traing data, also "floral marble" is present in all images, but its more generic si probably less powerfull. But as an alternative use "in the style of flrmrbl" or "flrmrbl style". Have fun!
a2eca7cfc4b3216175b4b668bb91edea
NinaXiao/distilroberta-base-finetuned-wikitext2
NinaXiao
roberta
8
2
transformers
0
fill-mask
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,267
false
<!-- 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9947 ## 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 | 285 | 2.0524 | | 2.2183 | 2.0 | 570 | 1.9742 | | 2.2183 | 3.0 | 855 | 1.9947 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
3f4c37c9f9e3bd2be6cd8e4244c37bc4
rafiuddin/t5-end2end-questions-generation
rafiuddin
t5
6
1
transformers
0
text2text-generation
true
false
false
apache-2.0
null
['squad_modified_for_t5_qg']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
993
false
<!-- 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-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
33b86b0734902b0d4d6d751bb5c90ffd
Cinnamomo/unico
Cinnamomo
null
28
0
null
26
text-to-image
false
false
false
creativeml-openrail-m
['en']
null
null
0
0
0
0
1
0
1
['stable-diffusion', 'text-to-image']
false
true
true
4,278
false
# i modelli 'Unico' <img src=https://i.imgur.com/5KfDOik.png width=100% height=100%> Unico is the series of custom mixed models. Based on Inizio Unico and AbyssOrange2 models with U-Net Merge, and support .safetensors format only. [WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)-amicable ## Summary This model repository includes 5 main models currently: 1. | Model: A | Model: B | Merge Weight | Base alpha | Merge Name | | --- | --- | --- | --- | --- | | [Inizio Fantasma+Inizio Inseguitore+Inizio Foschia](https://huggingface.co/Cinnamomo/inizio) | [Inizio Replicante+Inizio Skinjob+Inizio Deckard](https://huggingface.co/Cinnamomo/inizio) | weighted, M=0.66666666+M=0.66666666 | N/A | *Unico* | Unico is another form of [Inizio Unico](https://huggingface.co/Cinnamomo/inizio). 2. | Model: A | Model: B | Merge Weight | Base alpha | Merge Name | | --- | --- | --- | --- | --- | | [Inizio Unico](https://huggingface.co/Cinnamomo/inizio) | [AbyssOrange2 SFW](https://huggingface.co/WarriorMama777/OrangeMixs) | weighted, M=0.75. | N/A | *Unico Arancia* | Unico Arancia('Orange🍊') is the closest model from AbyssOrange2 SFW. Anime~Semi-realistic. 3. | Model: A | Model: B | U-Net Merge Weight | Base alpha | Merge Name | | --- | --- | --- | --- | --- | | Unico Arancia | [Openniji](https://huggingface.co/Korakoe/OpenNiji) | 1,1,1,1,0,0,1,1,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,1 | 0 | *Unico Bergamotto* | Unico Bergamotto('Bergamot🍊') is significantly improved model of Unico Arancia for lightning and hand details. Anime~Semi-realistic. 4. | Model: A | Model: B | U-Net Merge Weight | Base alpha | Merge Name | | --- | --- | --- | --- | --- | | Unico Vaniglia | [Openniji](https://huggingface.co/Korakoe/OpenNiji) | 1,1,1,1,0,0,1,1,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,1 | 0 | *Unico Vaniglia 1.5* | Unico Vaniglia('Vanilla🍦') 1.5 is significantly improved model of Unico Vaniglia for lightning and hand details. Anime~Semi-realistic. - NOTE: Another models are moved to legacy folder. ## Setting Reccomendation ``` ##Basic prompts for anime "txt2img/Prompt/value": "(best quality, extreme intricate detailed, octane render, very delicate cinematic light, colourful), (/*place tags*/), (solo girl/*character tags*/), (/*pose tags*/), (big breasts, big pelvis, slim waist, long legs, best ratio four finger and one thumb, /*body tags*/), (beautiful eyes and smooth radiant face, bishoujo), (/*colour of hair tag*/ hair, /*colour of eyes*/ eyes, thick lips, lip gloss), (/*clothing tags*/)", "txt2img/Negative prompt/value": "(nsfw, worst quality, low quality:1.4), (greyscale), (fingers(missing, fused, interlocked, abnormal, too many, bad anatomy, fused, fusion, lose, bad detailed, mutated), digit(extra, fewer), hands(greater than 4 fingers, less than 4 fingers, cropped, mutated):1.4), (fat, chubby, curvy, watermark, signature:1.4), (3d, realistic)" ``` - Variational Automatic Encoder: [SD MSE 840k.vae.safetensors](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors) - Clip Skip: 2 - Resolution: 1024x576 w/ HighRes. Fix - HighRes. Fix: R-ESRGAN General WDN 4xV3; upscale by 1.25 ## Sample Images > > <img src=https://i.imgur.com/gYTQvrw.png > width=100% height=100%> > ▲ X/Y Plot #1 > > <img src=https://i.imgur.com/HL6TfkD.png > width=100% height=100%> > ▲ X/Y Plot #2 > > <img src=https://i.imgur.com/vrZmSLY.png > width=100% height=100%> > ▲ X/Y Plot #3 > > <img src=https://i.imgur.com/aYIyVFJ.png > width=100% height=100%> > > <img src=https://i.imgur.com/pKNd2XO.png > width=100% height=100%> > > <img src=https://i.imgur.com/GknH4e0.png > width=100% height=100%> > > <img src=https://i.imgur.com/rVblL4d.png > width=100% height=100%> > ▲ Unico Arancia > > <img src=https://i.imgur.com/8vCjbUK.png > width=100% height=100%> > > <img src=https://i.imgur.com/HKvXAFx.png > width=100% height=100%> > ▲ Unico Bergamotto ## License Information This model follows Creative ML Open RAIL-M: [Stable Diffusion License](https://huggingface.co/spaces/CompVis/stable-diffusion-license) But, You may use whatever you want. I don't like to set such restriction. ## Contact *vitorriofungi@gmail.com* or [*Find Cinnamomo on Arcalive AI Art Channel*](https://arca.live/b/aiart)
b98be8a2fbab379a7ad3a1f873dc8ff4
erkam/sd-pokemon-model-lora
erkam
null
88
0
diffusers
0
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
0
0
0
0
0
0
0
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']
false
true
true
403
false
# LoRA text2image fine-tuning - https://huggingface.co/erkam/sd-pokemon-model-lora These are LoRA adaption weights for https://huggingface.co/erkam/sd-pokemon-model-lora. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
3b7087edbdc98b684f9040e6985e7ebb
saattrupdan/xlmr-base-texas-squad-de
saattrupdan
xlm-roberta
12
7
transformers
0
question-answering
true
false
false
mit
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
990
false
# TExAS-SQuAD-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-de dataset. It achieves the following results on the evaluation set: - Exact match: 61.45% - F1-score: 66.12% ## 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 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.8084 | 1.0 | 4233 | 1.5897 | | 1.5696 | 2.0 | 8466 | 1.5478 | | 1.4196 | 3.0 | 12699 | 1.5754 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.8.1+cu101 - Datasets 1.12.1 - Tokenizers 0.10.3
ba7e34ca964ec3b232a8635646e7a416
shivarama23/swin-tiny-patch4-window7-224-finetuned-image_quality
shivarama23
swin
16
32
transformers
1
image-classification
true
false
false
apache-2.0
null
['image_folder']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,934
false
<!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-image_quality This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.5242 - Accuracy: 0.9091 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.6762 | 0.6364 | | No log | 2.0 | 2 | 0.6309 | 0.7273 | | No log | 3.0 | 3 | 0.6095 | 0.6364 | | No log | 4.0 | 4 | 0.5775 | 0.6364 | | No log | 5.0 | 5 | 0.5443 | 0.8182 | | No log | 6.0 | 6 | 0.5242 | 0.9091 | | No log | 7.0 | 7 | 0.5149 | 0.8182 | | No log | 8.0 | 8 | 0.5094 | 0.8182 | | No log | 9.0 | 9 | 0.5038 | 0.8182 | | 0.4095 | 10.0 | 10 | 0.4992 | 0.8182 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
bbcc2b9f394a54b9b7c59aa92461d892
jayantapaul888/smalldata-microsoft-deberta-base-mnli-eng-only-sentiment-single-finetuned-memes
jayantapaul888
deberta
15
4
transformers
0
text-classification
true
false
false
mit
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,989
false
<!-- 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. --> # smalldata-microsoft-deberta-base-mnli-eng-only-sentiment-single-finetuned-memes This model is a fine-tuned version of [jayantapaul888/twitter-data-microsoft-deberta-base-mnli-sentiment-finetuned-memes](https://huggingface.co/jayantapaul888/twitter-data-microsoft-deberta-base-mnli-sentiment-finetuned-memes) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7400 - Accuracy: 0.8816 - Precision: 0.8946 - Recall: 0.8937 - F1: 0.8937 ## 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: 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: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 378 | 0.2962 | 0.8764 | 0.8917 | 0.8881 | 0.8884 | | 0.3387 | 2.0 | 756 | 0.2803 | 0.8831 | 0.8950 | 0.8942 | 0.8946 | | 0.1693 | 3.0 | 1134 | 0.4289 | 0.8764 | 0.8912 | 0.8892 | 0.8886 | | 0.0772 | 4.0 | 1512 | 0.5436 | 0.8690 | 0.8822 | 0.8823 | 0.8822 | | 0.0772 | 5.0 | 1890 | 0.6566 | 0.8831 | 0.8960 | 0.8947 | 0.8949 | | 0.024 | 6.0 | 2268 | 0.7400 | 0.8816 | 0.8946 | 0.8937 | 0.8937 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
194003f8df9464e82b5a7c790e973f2b
Kevincp560/bigbird-pegasus-large-arxiv-finetuned-pubmed
Kevincp560
bigbird_pegasus
10
1
transformers
0
text2text-generation
true
false
false
apache-2.0
null
['pub_med_summarization_dataset']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,921
false
<!-- 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. --> # bigbird-pegasus-large-arxiv-finetuned-pubmed This model is a fine-tuned version of [google/bigbird-pegasus-large-arxiv](https://huggingface.co/google/bigbird-pegasus-large-arxiv) on the pub_med_summarization_dataset dataset. It achieves the following results on the evaluation set: - Loss: 1.6049 - Rouge1: 45.4807 - Rouge2: 20.0199 - Rougel: 28.3621 - Rougelsum: 41.4618 - Gen Len: 219.144 ## 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 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.594 | 1.0 | 500 | 1.9879 | 33.6364 | 13.5074 | 21.4286 | 29.7158 | 189.014 | | 1.9146 | 2.0 | 1000 | 1.6494 | 44.0056 | 19.0069 | 27.5142 | 40.0492 | 210.528 | | 1.7378 | 3.0 | 1500 | 1.6213 | 44.7071 | 19.3559 | 27.6806 | 40.6124 | 213.596 | | 1.692 | 4.0 | 2000 | 1.6081 | 45.1505 | 19.7355 | 28.06 | 41.0108 | 213.674 | | 1.6656 | 5.0 | 2500 | 1.6049 | 45.4807 | 20.0199 | 28.3621 | 41.4618 | 219.144 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6
93be379e38e97cd0fe3d7fe703efc0bd
kazandaev/opus-mt-en-ru-finetuned_v2
kazandaev
marian
25
3
transformers
0
text2text-generation
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,583
false
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ru-finetuned_v2 This model is a fine-tuned version of [kazandaev/opus-mt-en-ru-finetuned_v2](https://huggingface.co/kazandaev/opus-mt-en-ru-finetuned_v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8471 - Bleu: 37.5148 - Gen Len: 29.8495 ## 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-06 - train_batch_size: 49 - eval_batch_size: 24 - 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 | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| | 0.7688 | 1.0 | 50906 | 0.8533 | 37.1941 | 29.8644 | | 0.764 | 2.0 | 101812 | 0.8504 | 37.1506 | 29.8481 | | 0.7637 | 3.0 | 152718 | 0.8485 | 37.3499 | 29.7743 | | 0.7593 | 4.0 | 203624 | 0.8477 | 37.4428 | 29.8165 | | 0.7579 | 5.0 | 254530 | 0.8471 | 37.5148 | 29.8495 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
c9fc70f1dcbe68979154a0b8652a0e06
Graphcore/bert-large-uncased
Graphcore
bert
13
1
transformers
6
null
true
false
false
apache-2.0
null
['Graphcore/wikipedia-bert-128', 'Graphcore/wikipedia-bert-512']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
5,734
false
# Graphcore/bert-large-uncased Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. ## Model description BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. It was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations. It reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks. ## Intended uses & limitations This model is a pre-trained BERT-Large trained in two phases on the [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) and [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) datasets. ## Training and evaluation data Trained on wikipedia datasets: - [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) - [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) ## Training procedure Trained MLM and NSP pre-training scheme from [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/abs/1904.00962). Trained on 64 Graphcore Mk2 IPUs using [`optimum-graphcore`](https://github.com/huggingface/optimum-graphcore) Command lines: Phase 1: ``` python examples/language-modeling/run_pretraining.py \ --config_name bert-large-uncased \ --tokenizer_name bert-large-uncased \ --ipu_config_name Graphcore/bert-large-ipu \ --dataset_name Graphcore/wikipedia-bert-128 \ --do_train \ --logging_steps 5 \ --max_seq_length 128 \ --max_steps 10550 \ --is_already_preprocessed \ --dataloader_num_workers 64 \ --dataloader_mode async_rebatched \ --lamb \ --lamb_no_bias_correction \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 512 \ --pod_type pod64 \ --learning_rate 0.006 \ --lr_scheduler_type linear \ --loss_scaling 32768 \ --weight_decay 0.01 \ --warmup_ratio 0.28 \ --config_overrides "layer_norm_eps=0.001" \ --ipu_config_overrides "matmul_proportion=[0.14 0.19 0.19 0.19]" \ --output_dir output-pretrain-bert-large-phase1 ``` Phase 2: ``` python examples/language-modeling/run_pretraining.py \ --config_name bert-large-uncased \ --tokenizer_name bert-large-uncased \ --model_name_or_path ./output-pretrain-bert-large-phase1 \ --ipu_config_name Graphcore/bert-large-ipu \ --dataset_name Graphcore/wikipedia-bert-512 \ --do_train \ --logging_steps 5 \ --max_seq_length 512 \ --max_steps 2038 \ --is_already_preprocessed \ --dataloader_num_workers 96 \ --dataloader_mode async_rebatched \ --lamb \ --lamb_no_bias_correction \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 512 \ --pod_type pod64 \ --learning_rate 0.002828 \ --lr_scheduler_type linear \ --loss_scaling 16384 \ --weight_decay 0.01 \ --warmup_ratio 0.128 \ --config_overrides "layer_norm_eps=0.001" \ --ipu_config_overrides "matmul_proportion=[0.14 0.19 0.19 0.19]" \ --output_dir output-pretrain-bert-large-phase2 ``` ### Training hyperparameters The following hyperparameters were used during phase 1 training: - learning_rate: 0.006 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 512 - total_train_batch_size: 65536 - total_eval_batch_size: 512 - optimizer: LAMB - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.28 - training_steps: 10550 - training precision: Mixed Precision The following hyperparameters were used during phase 2 training: - learning_rate: 0.002828 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 512 - total_train_batch_size: 16384 - total_eval_batch_size: 512 - optimizer: LAMB - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.128 - training_steps: 2038 - training precision: Mixed Precision ### Training results ``` train/epoch: 2.04 train/global_step: 2038 train/loss: 1.2002 train/train_runtime: 12022.3897 train/train_steps_per_second: 0.17 train/train_samples_per_second: 2777.367 ``` ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6
2f9c72bc7f5985b8697a4ee1d5159252
nicky007/stable-diffusion-trained-on-yujiro-hanma-images-baki-anime-fun-project
nicky007
null
16
66
diffusers
0
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
1
1
0
0
0
0
0
['text-to-image', 'stable-diffusion']
false
true
true
426
false
### Stable_Diffusion-trained-on-YUJIRO-HANMA-images(Baki-anime)-Fun-project model trained by nicky007 Trained on YUJIRO HANMA character of Baki-the grappler anime ..its just a fun project coz i was bored.. try Text on the prompt like: **'yujiro hanma clay statue'**, **'yujiro hanma laughing and angry pose'**, **'yujiro hanma posing very angry'** etc Or you can try your own unique text **Enjoy ,have a wonderfull day !!**
3b6c88b61314bdfc38358027de314e14
espnet/kan-bayashi_libritts_tts_train_gst_xvector_trasnformer_raw_phn_tacotro-truncated-250027
espnet
null
25
1
espnet
0
text-to-speech
false
false
false
cc-by-4.0
['en']
['libritts']
null
0
0
0
0
0
0
0
['espnet', 'audio', 'text-to-speech']
false
true
true
1,874
false
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_tts_train_gst+xvector_trasnformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4409702/ This model was trained by kan-bayashi using libritts/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} } ```
f5a71214b2359a30c1536ccb90a5778c
jonatasgrosman/exp_w2v2t_es_wavlm_s26
jonatasgrosman
wavlm
10
3
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
['es']
['mozilla-foundation/common_voice_7_0']
null
0
0
0
0
0
0
0
['automatic-speech-recognition', 'es']
false
true
true
438
false
# exp_w2v2t_es_wavlm_s26 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
d623df325a740d552c18b99104957e25
sinu/IndoBERT-exam-qa
sinu
bert
12
9
transformers
0
question-answering
true
false
false
mit
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,272
false
<!-- 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. --> # IndoBERT-exam-qa This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.395 | 1.0 | 8202 | 1.3536 | | 1.1534 | 2.0 | 16404 | 1.4040 | | 1.3661 | 3.0 | 24606 | 1.8274 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
a39fc30aa5bed0c41af95acef7e97243
jonatasgrosman/exp_w2v2t_it_vp-sv_s149
jonatasgrosman
wav2vec2
10
7
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
['it']
['mozilla-foundation/common_voice_7_0']
null
0
0
0
0
0
0
0
['automatic-speech-recognition', 'it']
false
true
true
469
false
# exp_w2v2t_it_vp-sv_s149 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](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.
b524a7f0739844445540e488f39f2f28
tnkmr/sfi_convtasnet_td_mgf_musdb18hq
tnkmr
null
2,230
0
null
0
audio-to-audio
false
false
false
mit
['ja']
['MUSDB18-HQ']
null
4
0
4
0
0
0
0
['music', 'audio', 'audio-to-audio', 'SFI']
false
true
true
1,248
false
# Sampling-frequency-independent (SFI) Conv-TasNet trained with the MUSDB18-HQ dataset for music source separation This model was proposed in [our IEEE/ACM Trans. ASLP paper](https://doi.org/10.1109/TASLP.2022.3203907) and works well with untrained sampling frequencies by using sampling-frequency-independent convolutional layers with the time domain filter design. The latent analog filter is a modulated Gaussian filter. It was trained by Tomohiko Nakamura using [the codebase](https://github.com/TomohikoNakamura/sfi_convtasnet)). This model was trained with 32 kHz-sampled data but works well with untrained sampling frequencies (e.g., 8, 16 kHz). # License MIT # Citation Please cite the following paper. ``` @article{KSaito2022IEEEACMTASLP, author={Saito, Koichi and Nakamura, Tomohiko and Yatabe, Kohei and Saruwatari, Hiroshi}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, title = {Sampling-frequency-independent convolutional layer and its application to audio source separation}, year=2022, month=sep, volume=30, pages={2928--2943}, doi={10.1109/TASLP.2022.3203907}, } ``` # Contents - Four trained models (seed=40,42,44,47) - Evaluation results (json files obtained with the museval library)
7a7132c3b0acf9de48ee931b1fad06eb
cjvt/gpt-sl-base
cjvt
gpt2
8
110
transformers
0
text-generation
true
false
false
apache-2.0
['sl']
null
null
0
0
0
0
0
0
0
['pytorch', 'causal-lm']
false
true
true
1,029
false
# GPT-sl-base This model is a Slovene GPT model, based on the [bigscience workshop](https://github.com/bigscience-workshop/Megatron-DeepSpeed) fork of the Megatron. GPT-sl-base was trained on large Slovene corpora: Gigafida, KAS, slWaC, and MaCoCu. ## Model architecture GPT-sl-base has about 110 million parameters. It consists of 12 transformer layers with a dimension of 768. It has 16 attention heads and can process sequences up to 1024 tokens in length. The tokenizer was trained on a smaller subset of the corpora, and has the vocabulary of 60k tokens. ## Training The model was trained for about 20 epochs, a total of 390k steps or 102B tokens seen during training. | Step | Validation Perplexity | |:------:|:---------------------:| | 50000 | 26.801 | | 100000 | 25.574 | | 150000 | 24.773 | | 200000 | 24.099 | | 250000 | 23.336 | | 300000 | 22.607 | | 350000 | 22.329 | | 390000 | 22.293 |
2ded5e8db2cca925ef703750d69d5082
eugenetanjc/wav2vec_mle
eugenetanjc
wav2vec2
12
1
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
2,249
false
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec_mle This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3076 - Wer: 1.0 ## 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: 6 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 7.3604 | 3.33 | 30 | 4.4612 | 1.0 | | 4.502 | 6.67 | 60 | 4.5906 | 1.0 | | 4.2842 | 10.0 | 90 | 4.4217 | 1.0 | | 4.3833 | 13.33 | 120 | 4.3967 | 1.0 | | 4.2631 | 16.67 | 150 | 4.3469 | 1.0 | | 4.3357 | 20.0 | 180 | 4.3372 | 1.0 | | 4.3941 | 23.33 | 210 | 4.3187 | 1.0 | | 4.393 | 26.67 | 240 | 4.2981 | 1.0 | | 4.3619 | 30.0 | 270 | 4.3049 | 1.0 | | 4.3849 | 33.33 | 300 | 4.3138 | 1.0 | | 4.3186 | 36.67 | 330 | 4.3123 | 1.0 | | 4.3196 | 40.0 | 360 | 4.3097 | 1.0 | | 4.3212 | 43.33 | 390 | 4.3279 | 1.0 | | 4.3108 | 46.67 | 420 | 4.3249 | 1.0 | | 4.3112 | 50.0 | 450 | 4.3093 | 1.0 | | 4.2994 | 53.33 | 480 | 4.3198 | 1.0 | | 4.2958 | 56.67 | 510 | 4.3071 | 1.0 | | 4.2905 | 60.0 | 540 | 4.3076 | 1.0 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
2f1ccd57f6144b7a4159173e7f851a42
Kuaaangwen/bert-base-cased-finetuned-wikitext2
Kuaaangwen
bert
9
2
transformers
0
fill-mask
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,258
false
<!-- 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-cased-finetuned-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6212 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.8335 | 1.0 | 2393 | 1.7164 | | 1.738 | 2.0 | 4786 | 1.6589 | | 1.7029 | 3.0 | 7179 | 1.6216 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
8fc344391458ee375ab6f8f5f2a44a7e
jonatasgrosman/wav2vec2-xls-r-1b-french
jonatasgrosman
wav2vec2
24
43
transformers
5
automatic-speech-recognition
true
false
false
apache-2.0
['fr']
['mozilla-foundation/common_voice_8_0']
null
0
0
0
0
0
0
0
['automatic-speech-recognition', 'fr', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event']
true
true
true
3,092
false
# Fine-tuned XLS-R 1B model for speech recognition in French Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on French using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [MediaSpeech](https://www.openslr.org/108/), [Multilingual TEDx](http://www.openslr.org/100), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). 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, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) ## Usage Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-french") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "fr" MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-french" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["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) predicted_sentences = processor.batch_decode(predicted_ids) ``` ## Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-french --dataset mozilla-foundation/common_voice_8_0 --config fr --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-french --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr-1b-french, title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {F}rench}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-french}}, year={2022} } ```
f5a979e83291d25915710af60a5ae548
Sleoruiz/distilbert-base-uncased-finetuned-emotion
Sleoruiz
distilbert
14
1
transformers
0
text-classification
true
false
false
apache-2.0
null
['emotion']
null
1
1
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,337
false
<!-- 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: 0.2176 - Accuracy: 0.927 - F1: 0.9273 ## 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8252 | 1.0 | 250 | 0.3121 | 0.916 | 0.9140 | | 0.2471 | 2.0 | 500 | 0.2176 | 0.927 | 0.9273 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
091ba58af0f7e124a6a48605e907eb34
cobraxx/maherkou
cobraxx
null
18
3
diffusers
0
text-to-image
false
false
false
creativeml-openrail-m
null
null
null
1
1
0
0
0
0
0
['text-to-image', 'stable-diffusion']
false
true
true
610
false
### maherkou Dreambooth model trained by cobraxx 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) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:
cea626a0725e2a0f8e892e73d2fa90b5
jonatasgrosman/exp_w2v2t_et_vp-sv_s445
jonatasgrosman
wav2vec2
10
5
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
['et']
['mozilla-foundation/common_voice_7_0']
null
0
0
0
0
0
0
0
['automatic-speech-recognition', 'et']
false
true
true
469
false
# exp_w2v2t_et_vp-sv_s445 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](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.
3067d4fe1097374928b274104f8f490b
MultiBertGunjanPatrick/multiberts-seed-0-20k
MultiBertGunjanPatrick
bert
7
2
transformers
0
null
true
false
false
apache-2.0
['en']
['bookcorpus', 'wikipedia']
null
0
0
0
0
0
0
0
['exbert', 'multiberts', 'multiberts-seed-0']
false
true
true
6,479
false
# MultiBERTs Seed 0 Checkpoint 20k (uncased) Seed 0 intermediate checkpoint 20k 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-0](https://hf.co/multberts-seed-0). 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-0-20k') model = BertModel.from_pretrained("multiberts-seed-0-20k") 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>
74cf9c25ed07d8487a47fbc9801aa2c8
CarpetCleaningArlingtonTX/CarpetCleaningArlingtonTX
CarpetCleaningArlingtonTX
null
2
0
null
0
null
false
false
false
other
null
null
null
0
0
0
0
0
0
0
[]
false
true
true
449
false
Carpet Cleaning Arlington TX https://carpetcleaning-arlington-tx.com/ (817) 381-5072 At Rug Cleaning Plano in TX we likewise have a truck mounted cover cleaning framework. These versatile vehicles have a force to be reckoned with of hardware. They generally have these on them and they can finish any occupation properly. Whether it is a little home, an enormous house or a gigantic modern intricate, the undertaking is rarely too large or intense.
8a7990e09784ffb330cf660f3a526501
nepalprabin/xlm-roberta-base-finetuned-panx-de
nepalprabin
xlm-roberta
13
5
transformers
0
token-classification
true
false
false
mit
null
['xtreme']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,318
false
<!-- 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 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: 0.1392 - F1: 0.8649 ## 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: 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1616 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1419 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1392 | 0.8649 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
a3030c1cc5303dc35a15e29135493b4c
kksukk/hubert_zeroth_gpu_freeze
kksukk
hubert
9
3
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
null
['zeroth_korean_asr']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
13,354
false
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hubert_zeroth_gpu_freeze This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - Loss: 4.8310 - Wer: 1.0 ## 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: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:---:| | 26.2877 | 0.14 | 100 | 10.6810 | 1.0 | | 6.4696 | 0.29 | 200 | 4.8799 | 1.0 | | 4.841 | 0.43 | 300 | 4.8521 | 1.0 | | 4.8366 | 0.57 | 400 | 4.8736 | 1.0 | | 4.8311 | 0.72 | 500 | 4.8559 | 1.0 | | 4.8383 | 0.86 | 600 | 4.8601 | 1.0 | | 4.8288 | 1.01 | 700 | 4.8474 | 1.0 | | 4.8283 | 1.15 | 800 | 4.8436 | 1.0 | | 4.8283 | 1.29 | 900 | 4.8440 | 1.0 | | 4.8299 | 1.44 | 1000 | 4.8518 | 1.0 | | 4.8274 | 1.58 | 1100 | 4.8406 | 1.0 | | 4.8308 | 1.72 | 1200 | 4.8384 | 1.0 | | 4.8316 | 1.87 | 1300 | 4.8427 | 1.0 | | 4.8298 | 2.01 | 1400 | 4.8423 | 1.0 | | 4.8291 | 2.16 | 1500 | 4.8481 | 1.0 | | 4.8326 | 2.3 | 1600 | 4.8426 | 1.0 | | 4.83 | 2.44 | 1700 | 4.8362 | 1.0 | | 4.8286 | 2.59 | 1800 | 4.8424 | 1.0 | | 4.8269 | 2.73 | 1900 | 4.8362 | 1.0 | | 4.8234 | 2.87 | 2000 | 4.8452 | 1.0 | | 4.8179 | 3.02 | 2100 | 4.8416 | 1.0 | | 4.825 | 3.16 | 2200 | 4.8519 | 1.0 | | 4.8185 | 3.3 | 2300 | 4.8384 | 1.0 | | 4.827 | 3.45 | 2400 | 4.8519 | 1.0 | | 4.8316 | 3.59 | 2500 | 4.8467 | 1.0 | | 4.825 | 3.74 | 2600 | 4.8465 | 1.0 | | 4.8246 | 3.88 | 2700 | 4.8422 | 1.0 | | 4.8228 | 4.02 | 2800 | 4.8326 | 1.0 | | 4.8277 | 4.17 | 2900 | 4.8353 | 1.0 | | 4.822 | 4.31 | 3000 | 4.8349 | 1.0 | | 4.82 | 4.45 | 3100 | 4.8395 | 1.0 | | 4.8252 | 4.6 | 3200 | 4.8350 | 1.0 | | 4.8283 | 4.74 | 3300 | 4.8377 | 1.0 | | 4.8229 | 4.89 | 3400 | 4.8344 | 1.0 | | 4.8264 | 5.03 | 3500 | 4.8352 | 1.0 | | 4.8237 | 5.17 | 3600 | 4.8337 | 1.0 | | 4.8271 | 5.32 | 3700 | 4.8385 | 1.0 | | 4.8332 | 5.46 | 3800 | 4.8392 | 1.0 | | 4.8189 | 5.6 | 3900 | 4.8353 | 1.0 | | 4.8209 | 5.75 | 4000 | 4.8355 | 1.0 | | 4.8179 | 5.89 | 4100 | 4.8297 | 1.0 | | 4.821 | 6.03 | 4200 | 4.8505 | 1.0 | | 4.8243 | 6.18 | 4300 | 4.8371 | 1.0 | | 4.8224 | 6.32 | 4400 | 4.8378 | 1.0 | | 4.8261 | 6.47 | 4500 | 4.8368 | 1.0 | | 4.8233 | 6.61 | 4600 | 4.8326 | 1.0 | | 4.8252 | 6.75 | 4700 | 4.8364 | 1.0 | | 4.8247 | 6.9 | 4800 | 4.8438 | 1.0 | | 4.8139 | 7.04 | 4900 | 4.8435 | 1.0 | | 4.8204 | 7.18 | 5000 | 4.8398 | 1.0 | | 4.8197 | 7.33 | 5100 | 4.8382 | 1.0 | | 4.82 | 7.47 | 5200 | 4.8371 | 1.0 | | 4.8266 | 7.61 | 5300 | 4.8431 | 1.0 | | 4.826 | 7.76 | 5400 | 4.8390 | 1.0 | | 4.8216 | 7.9 | 5500 | 4.8381 | 1.0 | | 4.82 | 8.05 | 5600 | 4.8339 | 1.0 | | 4.8281 | 8.19 | 5700 | 4.8316 | 1.0 | | 4.8246 | 8.33 | 5800 | 4.8361 | 1.0 | | 4.8169 | 8.48 | 5900 | 4.8338 | 1.0 | | 4.8175 | 8.62 | 6000 | 4.8341 | 1.0 | | 4.8283 | 8.76 | 6100 | 4.8358 | 1.0 | | 4.8232 | 8.91 | 6200 | 4.8356 | 1.0 | | 4.8193 | 9.05 | 6300 | 4.8325 | 1.0 | | 4.8146 | 9.2 | 6400 | 4.8297 | 1.0 | | 4.8207 | 9.34 | 6500 | 4.8283 | 1.0 | | 4.8221 | 9.48 | 6600 | 4.8334 | 1.0 | | 4.8229 | 9.63 | 6700 | 4.8308 | 1.0 | | 4.8239 | 9.77 | 6800 | 4.8352 | 1.0 | | 4.8245 | 9.91 | 6900 | 4.8314 | 1.0 | | 4.8173 | 10.06 | 7000 | 4.8300 | 1.0 | | 4.8189 | 10.2 | 7100 | 4.8341 | 1.0 | | 4.8209 | 10.34 | 7200 | 4.8287 | 1.0 | | 4.823 | 10.49 | 7300 | 4.8320 | 1.0 | | 4.8226 | 10.63 | 7400 | 4.8273 | 1.0 | | 4.8241 | 10.78 | 7500 | 4.8308 | 1.0 | | 4.8177 | 10.92 | 7600 | 4.8316 | 1.0 | | 4.8235 | 11.06 | 7700 | 4.8274 | 1.0 | | 4.8188 | 11.21 | 7800 | 4.8290 | 1.0 | | 4.8183 | 11.35 | 7900 | 4.8355 | 1.0 | | 4.8226 | 11.49 | 8000 | 4.8312 | 1.0 | | 4.8209 | 11.64 | 8100 | 4.8307 | 1.0 | | 4.8208 | 11.78 | 8200 | 4.8300 | 1.0 | | 4.8221 | 11.93 | 8300 | 4.8281 | 1.0 | | 4.82 | 12.07 | 8400 | 4.8306 | 1.0 | | 4.8199 | 12.21 | 8500 | 4.8343 | 1.0 | | 4.8212 | 12.36 | 8600 | 4.8314 | 1.0 | | 4.8212 | 12.5 | 8700 | 4.8309 | 1.0 | | 4.8228 | 12.64 | 8800 | 4.8310 | 1.0 | | 4.8225 | 12.79 | 8900 | 4.8325 | 1.0 | | 4.8146 | 12.93 | 9000 | 4.8364 | 1.0 | | 4.8174 | 13.07 | 9100 | 4.8328 | 1.0 | | 4.816 | 13.22 | 9200 | 4.8338 | 1.0 | | 4.822 | 13.36 | 9300 | 4.8378 | 1.0 | | 4.8253 | 13.51 | 9400 | 4.8411 | 1.0 | | 4.8173 | 13.65 | 9500 | 4.8379 | 1.0 | | 4.8227 | 13.79 | 9600 | 4.8374 | 1.0 | | 4.8138 | 13.94 | 9700 | 4.8372 | 1.0 | | 4.8191 | 14.08 | 9800 | 4.8327 | 1.0 | | 4.8259 | 14.22 | 9900 | 4.8335 | 1.0 | | 4.8098 | 14.37 | 10000 | 4.8301 | 1.0 | | 4.8248 | 14.51 | 10100 | 4.8315 | 1.0 | | 4.8199 | 14.66 | 10200 | 4.8304 | 1.0 | | 4.8202 | 14.8 | 10300 | 4.8312 | 1.0 | | 4.8159 | 14.94 | 10400 | 4.8316 | 1.0 | | 4.8181 | 15.09 | 10500 | 4.8306 | 1.0 | | 4.8217 | 15.23 | 10600 | 4.8350 | 1.0 | | 4.8095 | 15.37 | 10700 | 4.8328 | 1.0 | | 4.8249 | 15.52 | 10800 | 4.8329 | 1.0 | | 4.8178 | 15.66 | 10900 | 4.8355 | 1.0 | | 4.8192 | 15.8 | 11000 | 4.8342 | 1.0 | | 4.8249 | 15.95 | 11100 | 4.8366 | 1.0 | | 4.8096 | 16.09 | 11200 | 4.8385 | 1.0 | | 4.8196 | 16.24 | 11300 | 4.8390 | 1.0 | | 4.8271 | 16.38 | 11400 | 4.8352 | 1.0 | | 4.8166 | 16.52 | 11500 | 4.8371 | 1.0 | | 4.8206 | 16.67 | 11600 | 4.8348 | 1.0 | | 4.817 | 16.81 | 11700 | 4.8347 | 1.0 | | 4.8165 | 16.95 | 11800 | 4.8386 | 1.0 | | 4.8159 | 17.1 | 11900 | 4.8376 | 1.0 | | 4.8202 | 17.24 | 12000 | 4.8374 | 1.0 | | 4.8157 | 17.39 | 12100 | 4.8370 | 1.0 | | 4.8175 | 17.53 | 12200 | 4.8405 | 1.0 | | 4.8189 | 17.67 | 12300 | 4.8321 | 1.0 | | 4.8167 | 17.82 | 12400 | 4.8322 | 1.0 | | 4.8229 | 17.96 | 12500 | 4.8353 | 1.0 | | 4.8179 | 18.1 | 12600 | 4.8322 | 1.0 | | 4.8183 | 18.25 | 12700 | 4.8379 | 1.0 | | 4.8151 | 18.39 | 12800 | 4.8375 | 1.0 | | 4.8211 | 18.53 | 12900 | 4.8355 | 1.0 | | 4.8241 | 18.68 | 13000 | 4.8352 | 1.0 | | 4.8185 | 18.82 | 13100 | 4.8350 | 1.0 | | 4.8175 | 18.97 | 13200 | 4.8352 | 1.0 | | 4.8094 | 19.11 | 13300 | 4.8337 | 1.0 | | 4.8149 | 19.25 | 13400 | 4.8344 | 1.0 | | 4.8131 | 19.4 | 13500 | 4.8386 | 1.0 | | 4.8227 | 19.54 | 13600 | 4.8350 | 1.0 | | 4.8175 | 19.68 | 13700 | 4.8325 | 1.0 | | 4.8204 | 19.83 | 13800 | 4.8344 | 1.0 | | 4.8228 | 19.97 | 13900 | 4.8322 | 1.0 | | 4.8177 | 20.11 | 14000 | 4.8365 | 1.0 | | 4.824 | 20.26 | 14100 | 4.8338 | 1.0 | | 4.8151 | 20.4 | 14200 | 4.8342 | 1.0 | | 4.8189 | 20.55 | 14300 | 4.8339 | 1.0 | | 4.8115 | 20.69 | 14400 | 4.8325 | 1.0 | | 4.8162 | 20.83 | 14500 | 4.8291 | 1.0 | | 4.8182 | 20.98 | 14600 | 4.8321 | 1.0 | | 4.8189 | 21.12 | 14700 | 4.8314 | 1.0 | | 4.8123 | 21.26 | 14800 | 4.8318 | 1.0 | | 4.8165 | 21.41 | 14900 | 4.8320 | 1.0 | | 4.8247 | 21.55 | 15000 | 4.8315 | 1.0 | | 4.8165 | 21.7 | 15100 | 4.8311 | 1.0 | | 4.8151 | 21.84 | 15200 | 4.8352 | 1.0 | | 4.8234 | 21.98 | 15300 | 4.8298 | 1.0 | | 4.8136 | 22.13 | 15400 | 4.8282 | 1.0 | | 4.8179 | 22.27 | 15500 | 4.8297 | 1.0 | | 4.8128 | 22.41 | 15600 | 4.8307 | 1.0 | | 4.8216 | 22.56 | 15700 | 4.8290 | 1.0 | | 4.8177 | 22.7 | 15800 | 4.8286 | 1.0 | | 4.8209 | 22.84 | 15900 | 4.8311 | 1.0 | | 4.8183 | 22.99 | 16000 | 4.8276 | 1.0 | | 4.8135 | 23.13 | 16100 | 4.8284 | 1.0 | | 4.8116 | 23.28 | 16200 | 4.8279 | 1.0 | | 4.8161 | 23.42 | 16300 | 4.8291 | 1.0 | | 4.8202 | 23.56 | 16400 | 4.8292 | 1.0 | | 4.8199 | 23.71 | 16500 | 4.8298 | 1.0 | | 4.8203 | 23.85 | 16600 | 4.8293 | 1.0 | | 4.8177 | 23.99 | 16700 | 4.8286 | 1.0 | | 4.8153 | 24.14 | 16800 | 4.8273 | 1.0 | | 4.8202 | 24.28 | 16900 | 4.8260 | 1.0 | | 4.8189 | 24.43 | 17000 | 4.8289 | 1.0 | | 4.8219 | 24.57 | 17100 | 4.8279 | 1.0 | | 4.8148 | 24.71 | 17200 | 4.8284 | 1.0 | | 4.8113 | 24.86 | 17300 | 4.8286 | 1.0 | | 4.8133 | 25.0 | 17400 | 4.8299 | 1.0 | | 4.8164 | 25.14 | 17500 | 4.8309 | 1.0 | | 4.8231 | 25.29 | 17600 | 4.8279 | 1.0 | | 4.8135 | 25.43 | 17700 | 4.8296 | 1.0 | | 4.8118 | 25.57 | 17800 | 4.8293 | 1.0 | | 4.8139 | 25.72 | 17900 | 4.8279 | 1.0 | | 4.8144 | 25.86 | 18000 | 4.8281 | 1.0 | | 4.8207 | 26.01 | 18100 | 4.8284 | 1.0 | | 4.8096 | 26.15 | 18200 | 4.8285 | 1.0 | | 4.8177 | 26.29 | 18300 | 4.8275 | 1.0 | | 4.8221 | 26.44 | 18400 | 4.8288 | 1.0 | | 4.8147 | 26.58 | 18500 | 4.8281 | 1.0 | | 4.8148 | 26.72 | 18600 | 4.8281 | 1.0 | | 4.819 | 26.87 | 18700 | 4.8282 | 1.0 | | 4.8138 | 27.01 | 18800 | 4.8297 | 1.0 | | 4.8094 | 27.16 | 18900 | 4.8291 | 1.0 | | 4.8236 | 27.3 | 19000 | 4.8288 | 1.0 | | 4.8208 | 27.44 | 19100 | 4.8292 | 1.0 | | 4.816 | 27.59 | 19200 | 4.8279 | 1.0 | | 4.8103 | 27.73 | 19300 | 4.8290 | 1.0 | | 4.8152 | 27.87 | 19400 | 4.8296 | 1.0 | | 4.8158 | 28.02 | 19500 | 4.8304 | 1.0 | | 4.8122 | 28.16 | 19600 | 4.8293 | 1.0 | | 4.8199 | 28.3 | 19700 | 4.8293 | 1.0 | | 4.8185 | 28.45 | 19800 | 4.8287 | 1.0 | | 4.8198 | 28.59 | 19900 | 4.8294 | 1.0 | | 4.8102 | 28.74 | 20000 | 4.8291 | 1.0 | | 4.8168 | 28.88 | 20100 | 4.8290 | 1.0 | | 4.8117 | 29.02 | 20200 | 4.8303 | 1.0 | | 4.8156 | 29.17 | 20300 | 4.8295 | 1.0 | | 4.8127 | 29.31 | 20400 | 4.8298 | 1.0 | | 4.8193 | 29.45 | 20500 | 4.8301 | 1.0 | | 4.8174 | 29.6 | 20600 | 4.8301 | 1.0 | | 4.8167 | 29.74 | 20700 | 4.8301 | 1.0 | | 4.8137 | 29.89 | 20800 | 4.8310 | 1.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.0.0 - Tokenizers 0.13.2
2596bf021ff2ee77b3b8df6a0138eb31
spacy/hr_core_news_md
spacy
null
32
1
spacy
0
token-classification
false
false
false
cc-by-sa-4.0
['hr']
null
null
0
0
0
0
0
0
0
['spacy', 'token-classification']
false
true
true
42,189
false
### Details: https://spacy.io/models/hr#hr_core_news_md Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `hr_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` | | **Vectors** | floret (50000, 300) | | **Sources** | [Training corpus hr500k 1.0](http://hdl.handle.net/11356/1183) (Ljubešić, Nikola ; Agić, Željko ; Klubička, Filip ; Batanović, Vuk and Erjavec, Tomaž)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (1518 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmpny`, `Agcmsany`, `Agcmsay`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnpay`, `Agcnpdy`, `Agcnpgy`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsin`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpfsvy`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmsan`, `Agpmsann`, `Agpmsany`, `Agpmsay`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsln`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpmsvy`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsln`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpdy`, `Agsfpgy`, `Agsfpiy`, `Agsfply`, `Agsfpny`, `Agsfsay`, `Agsfsdy`, `Agsfsgy`, `Agsfsiy`, `Agsfsly`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmpiy`, `Agsmply`, `Agsmpny`, `Agsmsany`, `Agsmsayn`, `Agsmsayy`, `Agsmsdy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnply`, `Agsnpny`, `Agsnsay`, `Agsnsdy`, `Agsnsgy`, `Agsnsiy`, `Agsnsly`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpdy`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsany`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgn`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpdy`, `Appnpgy`, `Appnpiy`, `Appnply`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfpiy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmply`, `Aspmpny`, `Aspmsayn`, `Aspmsayy`, `Aspmsdn`, `Aspmsdy`, `Aspmsgn`, `Aspmsgy`, `Aspmsiy`, `Aspmsln`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnpgy`, `Aspnpny`, `Aspnsay`, `Aspnsgn`, `Aspnsgy`, `Aspnsln`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--g`, `Mlc--i`, `Mlc--l`, `Mlcf-a`, `Mlcf-d`, `Mlcf-g`, `Mlcf-n`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-g`, `Mlcm-l`, `Mlcm-n`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsg`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsd`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-g`, `Mlsm-l`, `Mlsm-n`, `Mlsmpn`, `Mlsn-n`, `Mrc`, `Mro`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfpv`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncfsv`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmpv`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Ncnsv`, `Npfpa`, `Npfpg`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npmsv`, `Npnpg`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npg`, `Pd-npi`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npi`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsd`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-i`, `Pi3m-n`, `Pi3n-a`, `Pi3n-d`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-sg`, `Pp1-si`, `Pp1-sl`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pl`, `Pp2-pn`, `Pp2-sa`, `Pp2-sd`, `Pp2-sg`, `Pp2-sl`, `Pp2-sn`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsi`, `Pp3nsn`, `Pq-fpa`, `Pq-fpn`, `Pq-fsa`, `Pq-fsi`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3m-d`, `Pq3m-n`, `Pq3n-a`, `Pq3n-l`, `Pq3n-n`, `Ps1fpa`, `Ps1fpg`, `Ps1fpl`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsi`, `Ps1fsl`, `Ps1fsn`, _(truncated: full list in pipeline meta)_ | | **`morphologizer`** | `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Ins\|POS=ADP`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Degree=Pos\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `POS=PART`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=CCONJ`, `Case=Gen\|POS=ADP`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Degree=Pos\|POS=ADV\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `NumType=Ord\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Degree=Pos\|POS=ADV\|PronType=Int,Rel`, `Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=X`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=ADV\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=ADV\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=NOUN`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=SPACE`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Ins\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|POS=ADP`, `Degree=Sup\|POS=ADV`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `POS=ADV\|Tense=Pres\|VerbForm=Conv`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `NumType=Mult\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `POS=ADV\|Tense=Past\|VerbForm=Conv`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Degree=Pos\|POS=ADV\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Gen\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Mult\|POS=SYM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, _(truncated: full list in pipeline meta)_ | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl:pv`, `fixed`, `flat`, `flat:foreign`, `goeswith`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` | | **`ner`** | `DERIV_PER`, `LOC`, `MISC`, `ORG`, `PER` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.89 | | `TOKEN_P` | 97.28 | | `TOKEN_R` | 98.71 | | `TOKEN_F` | 97.99 | | `TAG_ACC` | 91.69 | | `POS_ACC` | 97.33 | | `MORPH_ACC` | 92.31 | | `MORPH_MICRO_P` | 95.98 | | `MORPH_MICRO_R` | 95.56 | | `MORPH_MICRO_F` | 95.77 | | `SENTS_P` | 95.12 | | `SENTS_R` | 93.41 | | `SENTS_F` | 94.25 | | `DEP_UAS` | 86.45 | | `DEP_LAS` | 80.05 | | `LEMMA_ACC` | 92.81 | | `ENTS_P` | 82.44 | | `ENTS_R` | 81.34 | | `ENTS_F` | 81.89 |
a9d1c84f9ba4b9f732b8400cbf847839
lschlessinger/bert-finetuned-math-prob-classification
lschlessinger
bert
12
1
transformers
0
text-classification
true
false
false
apache-2.0
null
['competition_math']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
2,334
false
# bert-finetuned-math-prob-classification This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the part of the [competition_math dataset](https://huggingface.co/datasets/competition_math). Specifically, it was trained as a multi-class multi-label model on the problem text. The problem types (labels) used here are "Counting & Probability", "Prealgebra", "Algebra", "Number Theory", "Geometry", "Intermediate Algebra", and "Precalculus". ## Model description See the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model for more details. The only architectural modification made was to the classification head. Here, 7 classes were used. ## Intended uses & limitations This model is intended for demonstration purposes only. The problem type data was in English and contains many LaTeX tokens. ## Training and evaluation data The `problem` field of [competition_math dataset](https://huggingface.co/datasets/competition_math) was used for training and evaluation input data. The target data was taken from the `type` field. ## 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: 3.0 ### Training results This fine-tuned model achieves the following result on the problem type competition math test set: ``` precision recall f1-score support Algebra 0.78 0.79 0.79 1187 Counting & Probability 0.75 0.81 0.78 474 Geometry 0.76 0.83 0.79 479 Intermediate Algebra 0.86 0.84 0.85 903 Number Theory 0.79 0.82 0.80 540 Prealgebra 0.66 0.61 0.63 871 Precalculus 0.95 0.89 0.92 546 accuracy 0.79 5000 macro avg 0.79 0.80 0.79 5000 weighted avg 0.79 0.79 0.79 5000 ``` ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
c2ef2b60923c1802a89517fbcd9901dc
jonatasgrosman/exp_w2v2t_ja_xlsr-53_s109
jonatasgrosman
wav2vec2
10
5
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
['ja']
['mozilla-foundation/common_voice_7_0']
null
0
0
0
0
0
0
0
['automatic-speech-recognition', 'ja']
false
true
true
461
false
# exp_w2v2t_ja_xlsr-53_s109 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (ja)](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.
5c9031be8d9a9480b1a541e3ac3afbd4
jiobiala24/wav2vec2-base-checkpoint-2
jiobiala24
wav2vec2
15
8
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
null
['common_voice']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,531
false
<!-- 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-TPU-cv-fine-tune-2 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-TPU-cv-fine-tune](https://huggingface.co/jiobiala24/wav2vec2-base-TPU-cv-fine-tune) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.6051 - Wer: 0.5484 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.522 | 6.45 | 400 | 1.2550 | 0.5649 | | 0.2874 | 12.9 | 800 | 1.4235 | 0.6054 | | 0.152 | 19.35 | 1200 | 1.5743 | 0.5806 | | 0.0857 | 25.8 | 1600 | 1.6051 | 0.5484 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
9ce7e1332c13985f52adcc350946ff00
edumunozsala/roberta_bne_sentiment_analysis_es
edumunozsala
roberta
11
162
transformers
2
text-classification
true
false
false
apache-2.0
['es']
['IMDbreviews_es']
null
1
0
1
0
0
0
0
['sagemaker', 'roberta-bne', 'TextClassification', 'SentimentAnalysis']
true
true
true
2,771
false
# Model roberta_bne_sentiment_analysis_es ## **A finetuned model for Sentiment analysis in Spanish** This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container, The base model is **RoBERTa-base-bne** which is a RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB. It was trained by The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) **RoBERTa BNE Citation** Check out the paper for all the details: https://arxiv.org/abs/2107.07253 ``` @article{gutierrezfandino2022, 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}, title = {MarIA: Spanish Language Models}, journal = {Procesamiento del Lenguaje Natural}, volume = {68}, number = {0}, year = {2022}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405}, pages = {39--60} } ``` ## Dataset The dataset is a collection of movie reviews in Spanish, about 50,000 reviews. The dataset is balanced and provides every review in english, in spanish and the label in both languages. Sizes of datasets: - Train dataset: 42,500 - Validation dataset: 3,750 - Test dataset: 3,750 ## Intended uses & limitations This model is intented for Sentiment Analysis for spanish corpus and finetuned specially for movie reviews but it can be applied to other kind of reviews. ## Hyperparameters { "epochs": "4", "train_batch_size": "32", "eval_batch_size": "8", "fp16": "true", "learning_rate": "3e-05", "model_name": "\"PlanTL-GOB-ES/roberta-base-bne\"", "sagemaker_container_log_level": "20", "sagemaker_program": "\"train.py\"", } ## Evaluation results - Accuracy = 0.9106666666666666 - F1 Score = 0.9090909090909091 - Precision = 0.9063852813852814 - Recall = 0.9118127381600436 ## Test results ## Model in action ### Usage for Sentiment Analysis ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es") model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es") text ="Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal" input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0) outputs = model(input_ids) output = outputs.logits.argmax(1) ``` Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala)
d927356df6f358778aacb03a698f32f6
Wiebke/bert-base-casedepoch3_sexist_baseline_with_reddit_and_gabfortest
Wiebke
bert
12
10
transformers
0
text-classification
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,060
false
<!-- 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-casedepoch3_sexist_baseline_with_reddit_and_gabfortest This model is a fine-tuned version of [Wiebke/bert-base-casedepoch3_sexist_baseline_with_reddit_and_gab](https://huggingface.co/Wiebke/bert-base-casedepoch3_sexist_baseline_with_reddit_and_gab) 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: 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 ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
6854e82e590efde22ceb1fce49625efc
akshaychaudhary/distilbert-base-uncased-finetuned-devops1-ner
akshaychaudhary
distilbert
13
5
transformers
0
token-classification
true
false
false
apache-2.0
null
null
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,559
false
<!-- 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-devops1-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9870 - Precision: 0.0572 - Recall: 0.2689 - F1: 0.0944 - Accuracy: 0.7842 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 72 | 0.6027 | 0.0484 | 0.2269 | 0.0798 | 0.7861 | | No log | 2.0 | 144 | 0.8631 | 0.0573 | 0.2857 | 0.0955 | 0.7771 | | No log | 3.0 | 216 | 0.9870 | 0.0572 | 0.2689 | 0.0944 | 0.7842 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
49328b88c7d8f1ed5d970c4bb466a4bc
Ryna/wav2vec2-large-xlsr-53-Enlgish-FT-ASCEND-colab
Ryna
wav2vec2
12
3
transformers
0
automatic-speech-recognition
true
false
false
apache-2.0
null
['ascend']
null
0
0
0
0
0
0
0
['generated_from_trainer']
true
true
true
1,147
false
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-Enlgish-FT-ASCEND-colab This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the ascend dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 10000 - total_train_batch_size: 160000 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
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