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modelId
string
author
string
last_modified
timestamp[us, tz=UTC]
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likes
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library_name
string
tags
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string
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timestamp[us, tz=UTC]
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ColabPro/PPO-LunarLander-v2-v1
ColabPro
2022-05-16T22:03:54Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T22:02:56Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -4.65 +/- 21.40 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
espnet/english_male_ryanspeech_fastspeech2
espnet
2022-05-16T22:00:14Z
5
4
espnet
[ "espnet", "audio", "text-to-speech", "en", "dataset:ryanspeech", "license:cc-by-nc-4.0", "region:us" ]
text-to-speech
2022-05-10T18:13:25Z
--- tags: - espnet - audio - text-to-speech language: en datasets: - ryanspeech license: cc-by-nc-4.0 widget: - text: "This seems a very pleasant place, and I think I shall enjoy myself very much." --- ## RyanSpeech model (based on ESPnet2) ### `espnet/english_male_ryanspeech_fastspeech2` This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the vocoder separately from [here](https://drive.google.com/file/d/10GYvB_mIKzXzSjD67tSnBhknZRoBjsNb/view?usp=sharing) and then use the following code: ``` from espnet2.bin.tts_inference import Text2Speech from scipy.io.wavfile import write model = Text2Speech.from_pretrained( model_file="espnet/english_male_ryanspeech_fastspeech2", vocoder_file="path_to_vocoder/train_nodev_parallel_wavegan.v1.long/checkpoint-1000000steps.pkl" ) output = model("This is a simple test.") write("x.wav", 22050, output['wav'].numpy()) ``` ## Download the dataset You can download RyanSpeech dataset from [here](https://www.kaggle.com/datasets/roholazandie/ryanspeech) or here. ## TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space ngpu: 1 seed: 0 num_workers: 1 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 cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 1000 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 6 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null pretrain_path: [] pretrain_key: [] num_iters_per_epoch: 500 batch_size: 20 valid_batch_size: null batch_bins: 800000 valid_batch_bins: null train_shape_file: - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/text_shape.phn - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/speech_shape valid_shape_file: - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/text_shape.phn - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 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_no_dev/text - text - text - - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/tr_no_dev/durations - durations - text_int - - dump/raw/tr_no_dev/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/dev/text - text - text - - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/dev/durations - durations - text_int - - dump/raw/dev/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 1.0 scheduler: noamlr scheduler_conf: model_size: 384 warmup_steps: 4000 token_list: - <blank> - <unk> - AH0 - T - N - S - R - D - L - K - IH1 - M - EH1 - Z - DH - UW1 - AE1 - IH0 - AY1 - AH1 - W - . - P - F - IY1 - V - ER0 - AA1 - B - AO1 - HH - EY1 - IY0 - ',' - Y - NG - OW1 - G - AW1 - TH - SH - UH1 - '?' - ER1 - JH - CH - OW0 - OW2 - EH2 - IH2 - EY2 - AA2 - AE2 - AY2 - '''' - OY1 - UW0 - '!' - AO2 - EH0 - ZH - AH2 - AE0 - UW2 - AA0 - AY0 - IY2 - AW2 - AO0 - EY0 - ER2 - UH2 - '...' - AW0 - UH0 - OY2 - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: tacotron g2p: g2p_en_no_space feats_extract: fbank feats_extract_conf: fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 hop_length: 256 n_fft: 1024 win_length: null normalize: global_mvn normalize_conf: stats_file: exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/feats_stats.npz tts: fastspeech tts_conf: adim: 384 aheads: 2 elayers: 6 eunits: 1536 dlayers: 6 dunits: 1536 positionwise_layer_type: conv1d positionwise_conv_kernel_size: 3 duration_predictor_layers: 2 duration_predictor_chans: 384 duration_predictor_kernel_size: 3 postnet_layers: 5 postnet_filts: 5 postnet_chans: 256 use_masking: true use_scaled_pos_enc: true encoder_normalize_before: true decoder_normalize_before: true reduction_factor: 1 init_type: xavier_uniform init_enc_alpha: 1.0 init_dec_alpha: 1.0 transformer_enc_dropout_rate: 0.1 transformer_enc_positional_dropout_rate: 0.1 transformer_enc_attn_dropout_rate: 0.1 transformer_dec_dropout_rate: 0.1 transformer_dec_positional_dropout_rate: 0.1 transformer_dec_attn_dropout_rate: 0.1 pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list distributed: false ``` </details> ### Citing RyanSpeech ```BibTex @inproceedings{Zandie2021RyanSpeechAC, title={RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis}, author={Rohola Zandie and Mohammad H. Mahoor and Julia Madsen and Eshrat S. Emamian}, booktitle={Interspeech}, year={2021} } ```
ATH0/ppo-LunarLander-v2
ATH0
2022-05-16T21:43:43Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T21:43:12Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 280.92 +/- 14.67 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
evolvingstuff/gpt2-wikitext2
evolvingstuff
2022-05-16T21:25:11Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-05-16T20:30:35Z
--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1128 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 6.5628 | 1.0 | 2249 | 6.4705 | | 6.1956 | 2.0 | 4498 | 6.2012 | | 6.021 | 3.0 | 6747 | 6.1128 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
microsoft/swin-large-patch4-window7-224-in22k
microsoft
2022-05-16T19:59:30Z
450
2
transformers
[ "transformers", "pytorch", "tf", "swin", "image-classification", "vision", "dataset:imagenet-21k", "arxiv:2103.14030", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-21k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Swin Transformer (large-sized model) Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png) [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, SwinForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-large-patch4-window7-224-in22k") model = SwinForImageClassification.from_pretrained("microsoft/swin-large-patch4-window7-224-in22k") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-14030, author = {Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo}, title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, journal = {CoRR}, volume = {abs/2103.14030}, year = {2021}, url = {https://arxiv.org/abs/2103.14030}, eprinttype = {arXiv}, eprint = {2103.14030}, timestamp = {Thu, 08 Apr 2021 07:53:26 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
microsoft/swin-large-patch4-window7-224
microsoft
2022-05-16T19:58:33Z
3,115
1
transformers
[ "transformers", "pytorch", "tf", "swin", "image-classification", "vision", "dataset:imagenet-1k", "arxiv:2103.14030", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Swin Transformer (large-sized model) Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png) [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, SwinForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-large-patch4-window7-224") model = SwinForImageClassification.from_pretrained("microsoft/swin-large-patch4-window7-224") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-14030, author = {Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo}, title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, journal = {CoRR}, volume = {abs/2103.14030}, year = {2021}, url = {https://arxiv.org/abs/2103.14030}, eprinttype = {arXiv}, eprint = {2103.14030}, timestamp = {Thu, 08 Apr 2021 07:53:26 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
Ukhushn/DistilHomeDepot-finetuned
Ukhushn
2022-05-16T19:16:36Z
4
0
transformers
[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-05-09T06:37:59Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Ukhushn/DistilHomeDepot-finetuned results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Ukhushn/DistilHomeDepot-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.6502 - Validation Loss: 2.2067 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1437, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.6502 | 2.2067 | 0 | ### Framework versions - Transformers 4.19.1 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1
KhariotnovKK/Car_racing_v0
KhariotnovKK
2022-05-16T19:02:47Z
3
0
stable-baselines3
[ "stable-baselines3", "CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T18:45:52Z
--- library_name: stable-baselines3 tags: - CarRacing-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 58.17 +/- 51.28 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CarRacing-v0 type: CarRacing-v0 --- # **PPO** Agent playing **CarRacing-v0** This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
maazmikail/finetuning-sentiment-model-urdu-roberta
maazmikail
2022-05-16T19:01:35Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-05-16T12:46:28Z
--- license: mit tags: - generated_from_trainer model-index: - name: finetuning-sentiment-model-urdu-roberta results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-urdu-roberta This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://huggingface.co/urduhack/roberta-urdu-small) 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: 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 ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
Rocketknight1/temp-colab-upload-test2
Rocketknight1
2022-05-16T18:59:35Z
5
0
transformers
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-23T17:02:59Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Rocketknight1/temp-colab-upload-test2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Rocketknight1/temp-colab-upload-test2 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6931 - Validation Loss: 0.6931 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6931 | 0.6931 | 0 | | 0.6931 | 0.6931 | 1 | ### Framework versions - Transformers 4.17.0 - TensorFlow 2.8.0 - Datasets 2.0.0 - Tokenizers 0.11.6
microsoft/swin-base-patch4-window12-384
microsoft
2022-05-16T18:32:57Z
28,937
4
transformers
[ "transformers", "pytorch", "tf", "swin", "image-classification", "vision", "dataset:imagenet-1k", "arxiv:2103.14030", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Swin Transformer (base-sized model) Swin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png) [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, SwinForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-base-patch4-window12-384") model = SwinForImageClassification.from_pretrained("microsoft/swin-base-patch4-window12-384") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-14030, author = {Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo}, title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, journal = {CoRR}, volume = {abs/2103.14030}, year = {2021}, url = {https://arxiv.org/abs/2103.14030}, eprinttype = {arXiv}, eprint = {2103.14030}, timestamp = {Thu, 08 Apr 2021 07:53:26 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
microsoft/swin-large-patch4-window12-384
microsoft
2022-05-16T18:08:30Z
697
1
transformers
[ "transformers", "pytorch", "tf", "swin", "image-classification", "vision", "dataset:imagenet-1k", "arxiv:2103.14030", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Swin Transformer (large-sized model) Swin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png) [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, SwinForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-large-patch4-window12-384") model = SwinForImageClassification.from_pretrained("microsoft/swin-large-patch4-window12-3844") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-14030, author = {Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo}, title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, journal = {CoRR}, volume = {abs/2103.14030}, year = {2021}, url = {https://arxiv.org/abs/2103.14030}, eprinttype = {arXiv}, eprint = {2103.14030}, timestamp = {Thu, 08 Apr 2021 07:53:26 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
importsmart/bert-to-distilbert-NER
importsmart
2022-05-16T18:02:27Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-05-16T17:45:16Z
--- license: mit tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-to-distilbert-NER results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.014488935721812434 - name: Recall type: recall value: 0.018512285425782565 - name: F1 type: f1 value: 0.016255356878971478 - name: Accuracy type: accuracy value: 0.7597280273150055 --- <!-- 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-to-distilbert-NER This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 44.0386 - Precision: 0.0145 - Recall: 0.0185 - F1: 0.0163 - Accuracy: 0.7597 ## 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: 6e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 201.4012 | 1.0 | 110 | 133.7231 | 0.0153 | 0.0106 | 0.0125 | 0.7539 | | 106.9317 | 2.0 | 220 | 99.3629 | 0.0266 | 0.0305 | 0.0284 | 0.7593 | | 81.3601 | 3.0 | 330 | 80.3763 | 0.0159 | 0.0214 | 0.0183 | 0.7604 | | 63.8325 | 4.0 | 440 | 67.7620 | 0.0179 | 0.0244 | 0.0207 | 0.7599 | | 52.0271 | 5.0 | 550 | 59.0806 | 0.0203 | 0.0268 | 0.0231 | 0.7598 | | 44.4419 | 6.0 | 660 | 55.3208 | 0.0211 | 0.0278 | 0.0240 | 0.7603 | | 39.2351 | 7.0 | 770 | 52.4510 | 0.0170 | 0.0222 | 0.0193 | 0.7598 | | 35.3438 | 8.0 | 880 | 50.4576 | 0.0205 | 0.0268 | 0.0232 | 0.7604 | | 32.7385 | 9.0 | 990 | 48.3418 | 0.0173 | 0.0227 | 0.0197 | 0.7595 | | 30.6531 | 10.0 | 1100 | 46.7304 | 0.0147 | 0.0188 | 0.0165 | 0.7600 | | 29.0811 | 11.0 | 1210 | 46.3386 | 0.0151 | 0.0190 | 0.0168 | 0.7599 | | 27.9501 | 12.0 | 1320 | 45.4516 | 0.0163 | 0.0204 | 0.0181 | 0.7604 | | 26.7452 | 13.0 | 1430 | 44.3425 | 0.0154 | 0.0199 | 0.0173 | 0.7592 | | 25.5367 | 14.0 | 1540 | 44.0415 | 0.0146 | 0.0190 | 0.0165 | 0.7594 | | 24.5507 | 15.0 | 1650 | 44.0386 | 0.0145 | 0.0185 | 0.0163 | 0.7597 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
BobBraico/bert-finetuned-mrpc
BobBraico
2022-05-16T17:13:31Z
3
0
transformers
[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-05-16T17:04:54Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert-finetuned-mrpc results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1719 - Train Accuracy: 0.9359 - Validation Loss: 0.4050 - Validation Accuracy: 0.8382 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1374, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.5877 | 0.6823 | 0.4665 | 0.8015 | 0 | | 0.3843 | 0.8201 | 0.4026 | 0.8309 | 1 | | 0.1719 | 0.9359 | 0.4050 | 0.8382 | 2 | ### Framework versions - Transformers 4.19.1 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1
mariastull/unit_1
mariastull
2022-05-16T16:56:24Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T16:55:05Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 225.02 +/- 24.01 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
subhasisj/ar-kd-XLM-minilmv2-32
subhasisj
2022-05-16T16:50:40Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]
question-answering
2022-05-16T10:49:04Z
--- tags: - generated_from_trainer model-index: - name: ar-kd-XLM-minilmv2-32 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ar-kd-XLM-minilmv2-32 This model is a fine-tuned version of [subhasisj/ar-TAPT-MLM-MiniLM](https://huggingface.co/subhasisj/ar-TAPT-MLM-MiniLM) 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
ThoDum/DQN-LunarLander-v2
ThoDum
2022-05-16T16:27:13Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T16:26:33Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -123.02 +/- 62.23 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **DQN** Agent playing **LunarLander-v2** This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
nouman10/robertabase-finetuned-claim-ltp-full-prompt_
nouman10
2022-05-16T16:23:35Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-05-16T16:09:03Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: nouman10/robertabase-finetuned-claim-ltp-full-prompt_ results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nouman10/robertabase-finetuned-claim-ltp-full-prompt_ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0334 - Validation Loss: 0.0237 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -427, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1997 | 0.0443 | 0 | | 0.0334 | 0.0237 | 1 | ### Framework versions - Transformers 4.19.1 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1
kingabzpro/Moonman-Lunar-Landing-v2
kingabzpro
2022-05-16T16:07:26Z
7
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-11T09:44:34Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 266.93 +/- 24.72 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed: ``` pip install stable-baselines3 pip install huggingface_sb3 ``` Then, you can use the model like this: ```python import gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy # Retrieve the model from the hub ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) ## filename = name of the model zip file from the repository checkpoint = load_from_hub(repo_id="kingabzpro/Moonman-Lunar-Landing-v2", filename="Moonman-Lunar-Landing-v2.zip") model = PPO.load(checkpoint) # Evaluate the agent eval_env = gym.make('LunarLander-v2') mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") # Watch the agent play obs = eval_env.reset() for i in range(1000): action, _state = model.predict(obs) obs, reward, done, info = eval_env.step(action) eval_env.render() if done: obs = eval_env.reset() eval_env.close() ```
vitouphy/wav2vec2-xls-r-1b-khmer
vitouphy
2022-05-16T16:04:46Z
28
1
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "openslr", "robust-speech-event", "km", "generated_from_trainer", "hf-asr-leaderboard", "dataset:openslr", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: - km license: apache-2.0 tags: - automatic-speech-recognition - openslr - robust-speech-event - km - generated_from_trainer - hf-asr-leaderboard datasets: - openslr model-index: - name: wav2vec2-xls-r-1b-km results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR km type: openslr args: km metrics: - name: Test WER type: wer value: 32.13 - name: Test CER type: cer value: 9.35 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: km metrics: - name: Test WER type: wer value: 32.13 - name: Test CER type: cer value: 9.35 --- # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the openslr dataset. It achieves the following results on the evaluation set: - Loss: 0.4239 - Wer: 0.4221 # Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py): - WER: 0.4490281634272114 - CER: 0.12198285179047481 # Evaluation results on OpenSLR "test" with LM ngram (self-split 10%) (Running ./eval.py): - WER: 0.32130107100357 - CER: 0.09345053678218891 # Note - Since this dataset is small (4 hours of voice recording), we decided not to train that for too long to avoid overfitting and under-generalization. - This model performs worse than its 300M-variant. Probably, we don't explore the hyper-parameter enough? ## Installation Install the following libraries on top of HuggingFace Transformers for the supports of language model. ``` pip install pyctcdecode pip install https://github.com/kpu/kenlm/archive/master.zip ``` ## Usage **Approach 1:** Using HuggingFace's pipeline, this will cover everything end-to-end from raw audio input to text output. ```python from transformers import pipeline # Load the model pipe = pipeline(model="vitouphy/wav2vec2-xls-r-300m-khmer") # Process raw audio output = pipe("sound_file.wav", chunk_length_s=10, stride_length_s=(4, 2)) ``` **Approach 2:** More custom way to predict phonemes. ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import librosa import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-khmer") model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-khmer") # Read and process the input speech_array, sampling_rate = librosa.load("sound_file.wav", sr=16_000) inputs = processor(speech_array, 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, axis=-1) predicted_sentences = processor.batch_decode(predicted_ids) print(predicted_sentences) ``` ## Intended uses & limitations The data used for this model is only around 4 hours of recordings. - We split into 80/10/10. Hence, the training hour is 3.2 hours, which is very very small. - Yet, its performance is not too bad. Quite interesting for such small dataset, actually. You can try it out. - Its limitation is: - Rare characters, e.g. ឬស្សី ឪឡឹក - Speech needs to be clear and articulate. - More data to cover more vocabulary and character may help improve this system. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 75 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5671 | 5.47 | 400 | 12.0218 | 1.0 | | 3.5159 | 10.95 | 800 | 10.6337 | 1.0 | | 2.4543 | 16.43 | 1200 | 1.8256 | 0.9839 | | 1.9437 | 21.91 | 1600 | 1.1237 | 0.9173 | | 1.696 | 27.39 | 2000 | 0.8246 | 0.7700 | | 1.5342 | 32.87 | 2400 | 0.6433 | 0.6594 | | 1.4509 | 38.35 | 2800 | 0.5500 | 0.5787 | | 1.3478 | 43.83 | 3200 | 0.5070 | 0.4907 | | 1.3096 | 49.31 | 3600 | 0.4692 | 0.4726 | | 1.2532 | 54.79 | 4000 | 0.4448 | 0.4479 | | 1.2291 | 60.27 | 4400 | 0.4374 | 0.4366 | | 1.196 | 65.75 | 4800 | 0.4314 | 0.4310 | | 1.1862 | 71.23 | 5200 | 0.4239 | 0.4221 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
APY/LunarLander
APY
2022-05-16T15:54:33Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T15:53:57Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 220.47 +/- 44.98 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
omar47/wav2vec2-large-xls-r-300m-urdu
omar47
2022-05-16T15:20:18Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-04-29T19:05:45Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-urdu results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-urdu This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m). It achieves the following results on the evaluation set: - Loss: 0.5285 - Wer: 0.1702 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 35 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 16.9618 | 0.74 | 32 | 15.0745 | 1.0 | | 9.1928 | 1.49 | 64 | 5.9361 | 1.0 | | 4.9307 | 2.23 | 96 | 4.2924 | 1.0 | | 3.8917 | 2.98 | 128 | 3.5873 | 1.0 | | 3.3867 | 3.72 | 160 | 3.2594 | 1.0 | | 3.2107 | 4.47 | 192 | 3.1718 | 1.0 | | 3.1395 | 5.21 | 224 | 3.1281 | 1.0 | | 3.115 | 5.95 | 256 | 3.1238 | 1.0 | | 3.0801 | 6.7 | 288 | 3.0674 | 1.0 | | 2.9725 | 7.44 | 320 | 2.8277 | 1.0 | | 2.4159 | 8.19 | 352 | 1.7186 | 0.9036 | | 1.3377 | 8.93 | 384 | 1.0271 | 0.6433 | | 0.8591 | 9.67 | 416 | 0.8087 | 0.5441 | | 0.726 | 10.42 | 448 | 0.7263 | 0.4634 | | 0.6242 | 11.16 | 480 | 0.6783 | 0.4156 | | 0.5417 | 11.91 | 512 | 0.6611 | 0.4305 | | 0.4784 | 12.65 | 544 | 0.6300 | 0.3926 | | 0.4198 | 13.4 | 576 | 0.5646 | 0.3499 | | 0.3798 | 14.14 | 608 | 0.5919 | 0.3229 | | 0.3356 | 14.88 | 640 | 0.5715 | 0.3369 | | 0.2954 | 15.63 | 672 | 0.5325 | 0.2728 | | 0.264 | 16.37 | 704 | 0.5535 | 0.2689 | | 0.2535 | 17.12 | 736 | 0.5467 | 0.2366 | | 0.2277 | 17.86 | 768 | 0.5219 | 0.2345 | | 0.2141 | 18.6 | 800 | 0.5314 | 0.2487 | | 0.2036 | 19.35 | 832 | 0.5382 | 0.2236 | | 0.2021 | 20.09 | 864 | 0.5038 | 0.1922 | | 0.1676 | 20.84 | 896 | 0.5238 | 0.2033 | | 0.1544 | 21.58 | 928 | 0.5069 | 0.1866 | | 0.1512 | 22.33 | 960 | 0.5045 | 0.1965 | | 0.1512 | 23.07 | 992 | 0.5167 | 0.1862 | | 0.1399 | 23.81 | 1024 | 0.5236 | 0.1840 | | 0.1291 | 24.56 | 1056 | 0.5234 | 0.1957 | | 0.1274 | 25.3 | 1088 | 0.5348 | 0.1943 | | 0.127 | 26.05 | 1120 | 0.4978 | 0.1719 | | 0.1105 | 26.79 | 1152 | 0.5067 | 0.1767 | | 0.1069 | 27.53 | 1184 | 0.5150 | 0.1758 | | 0.1058 | 28.28 | 1216 | 0.5218 | 0.1844 | | 0.0999 | 29.02 | 1248 | 0.5375 | 0.1852 | | 0.0964 | 29.77 | 1280 | 0.5373 | 0.1843 | | 0.0971 | 30.51 | 1312 | 0.5190 | 0.1776 | | 0.0906 | 31.26 | 1344 | 0.5217 | 0.1747 | | 0.0909 | 32.0 | 1376 | 0.5204 | 0.1778 | | 0.0784 | 32.74 | 1408 | 0.5336 | 0.1756 | | 0.0823 | 33.49 | 1440 | 0.5281 | 0.1699 | | 0.0834 | 34.23 | 1472 | 0.5292 | 0.1700 | | 0.0827 | 34.98 | 1504 | 0.5285 | 0.1702 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
espnet/kyrgyz_commonvoice_blstm
espnet
2022-05-16T15:20:01Z
2
0
espnet
[ "espnet", "audio", "automatic-speech-recognition", "ky", "dataset:commonvoice", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
automatic-speech-recognition
2022-05-16T15:19:16Z
--- tags: - espnet - audio - automatic-speech-recognition language: ky datasets: - commonvoice license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/kyrgyz_commonvoice_blstm` This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b pip install -e . cd egs2/commonvoice/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/kyrgyz_commonvoice_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Mon May 16 11:17:33 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b` - Commit date: `Thu Apr 28 19:50:59 2022 -0400` ## asr_train_asr_rnn_tr_raw_ky_bpe150_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_rnn_asr_model_valid.acc.ave/test_ky|1613|11040|95.0|4.2|0.8|0.5|5.4|18.4| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_rnn_asr_model_valid.acc.ave/test_ky|1613|77711|98.0|0.9|1.1|0.5|2.5|18.4| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_rnn_asr_model_valid.acc.ave/test_ky|1613|56384|97.6|1.4|1.1|0.6|3.1|18.3| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_rnn_tr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_rnn_tr_raw_ky_bpe150_sp ngpu: 1 seed: 0 num_workers: 1 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: 50 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - 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: false log_interval: null use_matplotlib: true use_tensorboard: true 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: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_ky_bpe150_sp/train/speech_shape - exp/asr_stats_raw_ky_bpe150_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_ky_bpe150_sp/valid/speech_shape - exp/asr_stats_raw_ky_bpe150_sp/valid/text_shape.bpe 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/train_ky_sp/wav.scp - speech - sound - - dump/raw/train_ky_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev_ky/wav.scp - speech - sound - - dump/raw/dev_ky/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adadelta optim_conf: lr: 0.1 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ▁ - р - л - н - к - а - т - и - . - е - о - м - у - ш - ы - й - ө - ү - п - з - с - да - б - ч - та - ▁жа - ',' - ке - ▁ка - ▁б - ▁ал - ар - ын - ▁э - те - ган - де - уу - ты - ды - ▁ба - ин - ▁а - га - ▁А - ▁ж - ма - ди - үн - г - я - ла - ат - гы - ▁с - ба - ▁ко - кан - ң - ти - ун - ▁Ал - ▁К - ▁кө - ду - сы - кы - чы - ги - на - ▁Б - в - до - чу - — - ▁ай - ары - ▁бир - ▁эле - ып - дө - үү - ▁М - ▁да - ▁же - ында - ча - гө - оо - дын - дар - ▁бер - э - ген - ▁менен - го - ▁са - тар - Ж - О - С - Т - ю - Э - ж - '?' - д - У - ф - И - Д - К - х - П - Н - ь - Ш - М - ц - Ч - Р - З - Ф - Г - '!' - Л - А - Б - Е - ӊ - '1' - '8' - ⁄ - Ю - Й - Ц - щ - Я - Ы - ъ - ё - – - Х - В - Ү - Ө - ” - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.5 use_preprocessor: true token_type: bpe bpemodel: data/ky_token_list/bpe_unigram150/bpe.model 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' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_ky_bpe150_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: vgg_rnn encoder_conf: rnn_type: lstm bidirectional: true use_projection: true num_layers: 4 hidden_size: 1024 output_size: 1024 postencoder: null postencoder_conf: {} decoder: rnn decoder_conf: num_layers: 2 hidden_size: 1024 sampling_probability: 0 att_conf: atype: location adim: 1024 aconv_chans: 10 aconv_filts: 100 required: - output_dir - token_list version: 0.10.6a1 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} } ```
huawei-noah/AutoTinyBERT-KD-S4
huawei-noah
2022-05-16T15:14:43Z
2
0
transformers
[ "transformers", "pytorch", "license:other", "endpoints_compatible", "region:us" ]
null
2022-05-16T15:09:51Z
--- license: other --- Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base). AutoTinyBERT provides a model zoo that can meet different latency requirements.
bartelds/wav2vec2-large-ft-cgn-3hrs
bartelds
2022-05-16T14:59:59Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "speech", "nl", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-05-16T14:38:39Z
--- language: nl tags: - speech --- # Wav2Vec2-Large-ft-CGN-3hrs An English Wav2Vec2 model fine-tuned on Dutch. This model is created by fine-tuning [`facebook/wav2vec2-large`](https://huggingface.co/facebook/wav2vec2-large) model on 3 hours of Dutch speech from [Het Corpus Gesproken Nederlands](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/).
huawei-noah/AutoTinyBERT-S4
huawei-noah
2022-05-16T14:57:40Z
4
0
transformers
[ "transformers", "pytorch", "license:other", "endpoints_compatible", "region:us" ]
null
2022-05-16T14:54:54Z
--- license: other --- Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base). AutoTinyBERT provides a model zoo that can meet different latency requirements.
huawei-noah/AutoTinyBERT-S3
huawei-noah
2022-05-16T14:56:13Z
1
0
transformers
[ "transformers", "pytorch", "license:other", "endpoints_compatible", "region:us" ]
null
2022-05-16T14:52:15Z
--- license: other --- Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base). AutoTinyBERT provides a model zoo that can meet different latency requirements.
Vvek/ppo-LunarLander-v2
Vvek
2022-05-16T14:34:14Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T14:33:45Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 146.29 +/- 113.97 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
David-Tedesco/MLops
David-Tedesco
2022-05-16T14:17:19Z
0
0
null
[ "region:us" ]
null
2022-05-12T19:25:54Z
This project is made in the Epitech Tek4 cursus for the MLops project --- title: IOT emoji: 🐢 colorFrom: pink colorTo: pink sdk: streamlit sdk_version: 1.2.0 app_file: model.py pinned: false --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
jespern/TEST2ppo-LunarLander-v2
jespern
2022-05-16T14:11:42Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T14:11:05Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 236.68 +/- 25.22 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
lirondos/anglicisms-spanish-mbert
lirondos
2022-05-16T14:03:29Z
36
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "anglicisms", "loanwords", "borrowing", "codeswitching", "arxiv:2203.16169", "es", "dataset:coalas", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-28T13:26:51Z
--- language: - es license: cc-by-4.0 tags: - anglicisms # Example: audio - loanwords # Example: automatic-speech-recognition - borrowing # Example: speech - codeswitching # Example to specify a library: allennlp - arxiv:2203.16169 datasets: - coalas # Example: common_voice. Use dataset id from https://hf.co/datasets widget: - text: "Las fake news sobre la celebrity se reprodujeron por los 'mass media' en prime time." - text: "Me gusta el cine noir y el anime." - text: "Benching, estar en el banquillo de tu 'crush' mientras otro juega de titular." - text: "Recetas de noviembre para el batch cooking." - text: "Utilizaron técnicas de machine learning, big data o blockchain." --- # anglicisms-spanish-mbert This is a pretrained model for detecting unassimilated English lexical borrowings (a.k.a. anglicisms) on Spanish newswire. This model labels words of foreign origin (fundamentally from English) used in Spanish language, words such as *fake news*, *machine learning*, *smartwatch*, *influencer* or *streaming*. The model is a fine-tuned version of [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) trained on the [COALAS](https://github.com/lirondos/coalas/) corpus for the task of detecting lexical borrowings. The model considers two labels: * ``ENG``: For English lexical borrowings (*smartphone*, *online*, *podcast*) * ``OTHER``: For lexical borrowings from any other language (*boutique*, *anime*, *umami*) The model uses BIO encoding to account for multitoken borrowings. **⚠ This is not the best-performing model for this task.** For the best-performing model (F1=85.76) see [Flair model](https://huggingface.co/lirondos/anglicisms-spanish-flair-cs). ## Metrics (on the test set) The following table summarizes the results obtained on the test set of the [COALAS](https://github.com/lirondos/coalas/) corpus. | LABEL | Precision | Recall | F1 | |:-------|-----:|-----:|---------:| | ALL | 88.09 | 79.46 | 83.55 | | ENG | 88.44 | 82.16 | 85.19 | | OTHER | 37.5 | 6.52 | 11.11 | ## Dataset This model was trained on [COALAS](https://github.com/lirondos/coalas/), a corpus of Spanish newswire annotated with unassimilated lexical borrowings. The corpus contains 370,000 tokens and includes various written media written in European Spanish. The test set was designed to be as difficult as possible: it covers sources and dates not seen in the training set, includes a high number of OOV words (92% of the borrowings in the test set are OOV) and is very borrowing-dense (20 borrowings per 1,000 tokens). |Set | Tokens | ENG | OTHER | Unique | |:-------|-----:|-----:|---------:|---------:| |Training |231,126 |1,493 | 28 |380 | |Development |82,578 |306 |49 |316| |Test |58,997 |1,239 |46 |987| |**Total** |372,701 |3,038 |123 |1,683 | ## More info More information about the dataset, model experimentation and error analysis can be found in the paper: *[Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling](https://aclanthology.org/2022.acl-long.268/)*. ## How to use ``` from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("lirondos/anglicisms-spanish-mbert") model = AutoModelForTokenClassification.from_pretrained("lirondos/anglicisms-spanish-mbert") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = example = "Buscamos data scientist para proyecto de machine learning." borrowings = nlp(example) print(borrowings) ``` ## Citation If you use this model, please cite the following reference: ``` @inproceedings{alvarez-mellado-lignos-2022-detecting, title = "Detecting Unassimilated Borrowings in {S}panish: {A}n Annotated Corpus and Approaches to Modeling", author = "{\'A}lvarez-Mellado, Elena and Lignos, Constantine", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.268", pages = "3868--3888", abstract = "This work presents a new resource for borrowing identification and analyzes the performance and errors of several models on this task. We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings{---}words from one language that are introduced into another without orthographic adaptation{---}and use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and topic-varied than previous corpora available for this task. Our results show that a BiLSTM-CRF model fed with subword embeddings along with either Transformer-based embeddings pretrained on codeswitched data or a combination of contextualized word embeddings outperforms results obtained by a multilingual BERT-based model.", } ```
cuuupid/maeve-12-6-xsum
cuuupid
2022-05-16T13:55:59Z
8
0
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "en", "dataset:xsum", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-15T06:26:17Z
--- language: - en tags: - text2text-generation - pytorch license: "gpl-3.0" datasets: - xsum widget: - text: "President Biden met with Russia's Putin over the weekend to discuss a ceasefire in Ukraine." example_title: "Ukrainian Ceasefire" - text: "Acme Ventures recently led a seed round to provide over $2MM in funding to Aiko Mail, an AI startup tackling email." example_title: "VC Investment" - text: "In a shocking move, Florida has decided to formally secede from the United States, opting to sink into the Atlantic Ocean." example_title: "Florida secedes" --- # Maeve - XSUM Maeve is a language model that is similar to BART in structure but trained specially using a CAT (Conditionally Adversarial Transformer). This allows the model to learn to create long-form text from short entries with high degrees of control and coherence that are impossible to achieve with traditional transformers. This specific model has been trained on the XSUM dataset, and can invert summaries into full-length news articles. Feel free to try examples on the right!
prashanth/mbart-large-cc25-ge-hi-to-en
prashanth
2022-05-16T13:47:51Z
26
0
transformers
[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "generated_from_trainer", "dataset:hindi_english_machine_translation", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-15T18:42:50Z
--- tags: - generated_from_trainer datasets: - hindi_english_machine_translation metrics: - bleu model-index: - name: mbart-large-cc25-ge-hi-to-en results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: hindi_english_machine_translation type: hindi_english_machine_translation args: hi-en metrics: - name: Bleu type: bleu value: 0.1823 --- <!-- 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. --> # mbart-large-cc25-ge-hi-to-en This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the hindi_english_machine_translation dataset. It achieves the following results on the evaluation set: - Loss: 1.1000 - Bleu: 0.1823 - Gen Len: 1023.383 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:------:|:--------:| | 1.4078 | 1.0 | 135739 | 1.1000 | 0.1823 | 1023.383 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu102 - Datasets 1.18.0 - Tokenizers 0.12.1
Manaranjan/TEST2ppo-LunarLander-v2
Manaranjan
2022-05-16T13:24:37Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T12:49:17Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 196.09 +/- 31.85 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
subhasisj/zh-kd-XLM-minilmv2-4
subhasisj
2022-05-16T12:40:04Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T19:07:20Z
Multilingual MiniLMv2 fine-tuned using Knowledge Distillation with a XLM Roberta Base Teacher Model on ZH Language
nouman10/robertabase-finetuned-claim-ltp-full-prompt
nouman10
2022-05-16T11:49:28Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-05-16T10:45:36Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: nouman10/robertabase-finetuned-claim-ltp-full-prompt results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nouman10/robertabase-finetuned-claim-ltp-full-prompt This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0233 - Validation Loss: 0.0231 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -425, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1965 | 0.0452 | 0 | | 0.0321 | 0.0231 | 1 | | 0.0232 | 0.0231 | 2 | | 0.0232 | 0.0231 | 3 | | 0.0233 | 0.0231 | 4 | ### Framework versions - Transformers 4.19.1 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1
ml6team/mbart-large-cc25-cnn-dailymail-xsum-nl
ml6team
2022-05-16T11:41:07Z
30
4
transformers
[ "transformers", "pytorch", "mbart", "text2text-generation", "bart", "summarization", "nl", "dataset:ml6team/cnn_dailymail_nl", "dataset:ml6team/xsum_nl", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-03-02T23:29:05Z
--- language: - nl tags: - mbart - bart - summarization datasets: - ml6team/cnn_dailymail_nl - ml6team/xsum_nl pipeline_tag: summarization widget: - text: 'Het jongetje werd eind april met zwaar letsel naar het ziekenhuis gebracht in Maastricht. Drie weken later overleed het kindje als gevolg van het letsel. Onderzoek moet nog uitwijzen wat voor verwondingen de baby precies had en hoe hij gewond is geraakt. Daarnaast doet de politie onderzoek in de woning van de ouders. Het is nog niet duidelijk wanneer de onderzoeken zijn afgerond, meldt 1Limburg. De verdachten zitten in beperkingen en mogen alleen contact hebben met hun advocaat.' - text: 'Volgens De Vries gaat het om "de hoogste beloning die ooit is uitgeloofd in Nederland". De stichting heeft een website waar donateurs geld kunnen storten, schrijft NH Nieuws. Volgens De Vries is dit initiatief ook bedoeld voor andere zaken waar beloningen voor een gouden tip worden uitgereikt. "Het is dus niet eenmalig", aldus De Vries. Het is de eerste keer dat zoiets wordt opgezet, stelt hij: De 18-jarige Tanja Groen verdween spoorloos tijdens de ontgroeningsweek van de Universiteit Maastricht in augustus 1993. Ze werd voor het laatst gezien nadat ze was vertrokken van een feestje. De studente zou vandaag 46 jaar zijn geworden. Ook de ouders van Groen waren op de persconferentie aanwezig. "Het is vandaag de verjaardag van Tanja Groen, die haar ouders al 27 jaar niet meer hebben kunnen vieren, omdat zij eind augustus 1993 spoorloos is verdwenen", zei De Vries. "Haar ouders zitten in tergende onzekerheid. Ze geloven dat ze niet meer leeft. Maar die ene promille vreet aan ze. Ze hebben recht op duidelijkheid. Ze komen op leeftijd. Grootste angst is nooit te weten wat er met hun kind is gebeurd." De Vries wil dat het miljoen binnen een jaar is ingezameld. Als het bedrag na een jaar lager uitkomt, dan is dat de uit te loven beloning. Is het meer, dan zal de rest van het geld gebruikt worden in beloningen in andere zaken. Het initiatief wordt gesteund door de politie en justitie. De afgelopen jaren is er vaker uitgebreid naar sporen van Tanja Groen gezocht, maar die zoekacties hebben niets concreets opgeleverd. Vorige week werd opnieuw naar de vrouw gezocht, op de Strabrechtse Heide in Noord-Brabant. Ook die zoektocht leverde niets op.' --- # mbart-large-cc25-cnn-dailymail-xsum-nl ## Model description Finetuned version of [mbart](https://huggingface.co/facebook/mbart-large-cc25). We also wrote a **blog post** about this model [here](https://blog.ml6.eu/why-we-open-sourced-two-dutch-summarization-datasets-1047445abc97) ## Intended uses & limitations It's meant for summarizing Dutch news articles. #### How to use ```python import transformers undisputed_best_model = transformers.MBartForConditionalGeneration.from_pretrained( "ml6team/mbart-large-cc25-cnn-dailymail-xsum-nl" ) tokenizer = transformers.MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") summarization_pipeline = transformers.pipeline( task="summarization", model=undisputed_best_model, tokenizer=tokenizer, ) summarization_pipeline.model.config.decoder_start_token_id = tokenizer.lang_code_to_id[ "nl_XX" ] article = "Kan je dit even samenvatten alsjeblief." # Dutch summarization_pipeline( article, do_sample=True, top_p=0.75, top_k=50, min_length=50, early_stopping=True, truncation=True, )[0]["summary_text"] ``` ## Training data Finetuned [mbart](https://huggingface.co/facebook/mbart-large-cc25) with [this dataset](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl) and [this dataset](https://huggingface.co/datasets/ml6team/xsum_nl)
anes-saidi/aragpt2-base-finetuned-wikitext2
anes-saidi
2022-05-16T11:14:18Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-05-16T10:51:28Z
--- tags: - generated_from_trainer model-index: - name: aragpt2-base-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # aragpt2-base-finetuned-wikitext2 This model is a fine-tuned version of [aubmindlab/aragpt2-base](https://huggingface.co/aubmindlab/aragpt2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.0307 ## 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 | 387 | 5.1841 | | 5.9664 | 2.0 | 774 | 5.0627 | | 5.4166 | 3.0 | 1161 | 5.0307 | ### Framework versions - Transformers 4.11.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.10.3
araffin/RecurrentPPO-CarRacing-v0_2
araffin
2022-05-16T10:57:49Z
2
1
stable-baselines3
[ "stable-baselines3", "CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-16T10:55:12Z
--- library_name: stable-baselines3 tags: - CarRacing-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: RecurrentPPO results: - metrics: - type: mean_reward value: 896.87 +/- 23.31 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CarRacing-v0 type: CarRacing-v0 --- # **RecurrentPPO** Agent playing **CarRacing-v0** This is a trained model of a **RecurrentPPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code Using recurrent PPO implementation from SB3 contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/pull/53
jsunster/layoutlmv2-finetuned-cord
jsunster
2022-05-16T09:35:27Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-05-16T08:58:07Z
--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer model-index: - name: layoutlmv2-finetuned-cord results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlmv2-finetuned-cord This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.19.1 - Pytorch 1.10.0+cu111 - Datasets 2.2.1 - Tokenizers 0.12.1
SreyanG-NVIDIA/bert-base-cased-finetuned-squad
SreyanG-NVIDIA
2022-05-16T08:39:41Z
35
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-13T13:39:02Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-cased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0848 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.0337 | 1.0 | 5546 | 1.0150 | | 0.7546 | 2.0 | 11092 | 1.0015 | | 0.5537 | 3.0 | 16638 | 1.0848 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
nandezgarcia/roberta-base-bne-sqac-finetuned-recores
nandezgarcia
2022-05-16T08:07:43Z
2
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
multiple-choice
2022-05-16T07:52:17Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-sqac-finetuned-recores results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-sqac-finetuned-recores This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne-sqac](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4624 - Accuracy: 0.3691 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5643 | 1.0 | 1047 | 1.5474 | 0.3526 | | 0.8147 | 2.0 | 2094 | 2.6498 | 0.3719 | | 0.1618 | 3.0 | 3141 | 3.1061 | 0.3719 | | 0.0135 | 4.0 | 4188 | 3.4624 | 0.3691 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.1+cu102 - Datasets 2.2.1 - Tokenizers 0.12.1
withU/kogpt2-emotion-chatbot
withU
2022-05-16T07:58:01Z
237
4
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-05-12T05:21:44Z
# KoGPT2-emotion-chatbot kogpt2 on hugging face Transformers for Psychological Counseling - [full project link](https://github.com/jiminAn/Capstone_2022) ## how to use ``` from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast model = GPT2LMHeadModel.from_pretrained("withU/kogpt2-emotion-chatbot") tokenizer = PreTrainedTokenizerFast.from_pretrained("withU/kogpt2-emotion-chatbot") input_ids = tokenizer.encode("안녕", add_special_tokens=False, return_tensors="pt") output_sequences = model.generate(input_ids=input_ids, do_sample=True, max_length=80, num_return_sequences=4) for generated_sequence in output_sequences: generated_sequence = generated_sequence.tolist() print("GENERATED SEQUENCE : {0}".format(tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True))) ``` ## dataset finetuned on - [wellness dataset](https://aihub.or.kr/opendata/keti-data/recognition-laguage/KETI-02-006) - [emotion corpus of conversations](https://aihub.or.kr/opendata/keti-data/recognition-laguage/KETI-02-010) - [chatbot data](https://jeongukjae.github.io/tfds-korean/datasets/korean_chatbot_qa_data.html) ## references - [WelllnessConversation-LanguageModel](https://github.com/nawnoes/WellnessConversation-LanguageModel) - [KoGPT2: SKT-AI](https://github.com/SKT-AI/KoGPT2)
madatnlp/sk-kogptv2-kormath-causal
madatnlp
2022-05-16T07:56:43Z
8
0
transformers
[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2022-05-13T11:28:16Z
--- license: cc-by-nc-sa-4.0 tags: - generated_from_keras_callback model-index: - name: madatnlp/sk-kogptv2-kormath-causal results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # madatnlp/sk-kogptv2-kormath-causal This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3184 - Validation Loss: 1.4046 - Epoch: 15 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 2.2999999e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.7142 | 1.8683 | 0 | | 1.6077 | 1.4417 | 1 | | 1.2458 | 1.3161 | 2 | | 1.0396 | 1.2704 | 3 | | 0.8848 | 1.2818 | 4 | | 0.7634 | 1.2579 | 5 | | 0.6699 | 1.2724 | 6 | | 0.5948 | 1.2718 | 7 | | 0.5306 | 1.3300 | 8 | | 0.4832 | 1.3377 | 9 | | 0.4401 | 1.3038 | 10 | | 0.4053 | 1.3622 | 11 | | 0.3782 | 1.3577 | 12 | | 0.3550 | 1.3696 | 13 | | 0.3347 | 1.3682 | 14 | | 0.3184 | 1.4046 | 15 | ### Framework versions - Transformers 4.19.1 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1
hm192494/distilgpt2-finetuned-wikitext2
hm192494
2022-05-16T07:42:01Z
4
0
transformers
[ "transformers", "tf", "tensorboard", "roberta", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-05-14T22:18:41Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hm192494/distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hm192494/distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.1414 - Validation Loss: 5.4539 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 6.1414 | 5.4539 | 0 | ### Framework versions - Transformers 4.19.1 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1
ceggian/sbert_pt_reddit_softmax_256
ceggian
2022-05-16T06:52:11Z
2
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-05-16T06:48:33Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 117759 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 11775, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
kompactss/JeBERT_ko_je
kompactss
2022-05-16T06:11:24Z
5
0
transformers
[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-01T15:16:08Z
--- license: afl-3.0 --- # 🍊 제주 방언 번역 모델 🍊 - 표준어 -> 제주어 - Made by. 구름 자연어처리 과정 3기 3조!! - github link : https://github.com/Goormnlpteam3/JeBERT ## 1. Seq2Seq Transformer Model - encoder : BertConfig - decoder : BertConfig - Tokenizer : WordPiece Tokenizer ## 2. Dataset - Jit Dataset - AI HUB(+아래아 문자) ## 3. Hyper Parameters - Epoch : 10 epochs(best at 7 epoch) - Random Seed : 42 - Learning Rate : 5e-5 - Warm up Ratio : 0.1 - Batch Size : 32 ## 4. BLEU Score - Jit + AI HUB(+아래아 문자) Dataset : 67.3 --- ### CREDIT - 주형준 : [email protected] - 강가람 : [email protected] - 고광연 : [email protected] - 김수연 : [email protected] - 이원경 : [email protected] - 조성은 : [email protected]
kompactss/JeBERT_je_ko
kompactss
2022-05-16T06:11:10Z
4
0
transformers
[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-01T15:03:18Z
--- license: afl-3.0 --- # 🍊 제주 방언 번역 모델 🍊 - 제주어 -> 표준어 - Made by. 구름 자연어처리 과정 3기 3조!! - github link : https://github.com/Goormnlpteam3/JeBERT ## 1. Seq2Seq Transformer Model - encoder : BertConfig - decoder : BertConfig - Tokenizer : WordPiece Tokenizer ## 2. Dataset - Jit Dataset - AI HUB(+아래아 문자) ## 3. Hyper Parameters - Epoch : 10 epochs(best at 8 epoch) - Random Seed : 42 - Learning Rate : 5e-5 - Warm up Ratio : 0.1 - Batch Size : 32 ## 4. BLEU Score - Jit + AI HUB(+아래아 문자) Dataset : 79.0 --- ### CREDIT - 주형준 : [email protected] - 강가람 : [email protected] - 고광연 : [email protected] - 김수연 : [email protected] - 이원경 : [email protected] - 조성은 : [email protected]
kompactss/JeBERT_ko_je_v2
kompactss
2022-05-16T06:10:50Z
5
0
transformers
[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-02T17:30:31Z
--- license: afl-3.0 --- # 🍊 제주 방언 번역 모델 🍊 - 표준어 -> 제주어 - Made by. 구름 자연어처리 과정 3기 3조!! - github link : https://github.com/Goormnlpteam3/JeBERT ## 1. Seq2Seq Transformer Model - encoder : BertConfig - decoder : BertConfig - Tokenizer : WordPiece Tokenizer ## 2. Dataset - Jit Dataset - AI HUB(+아래아 문자)_v2 ## 3. Hyper Parameters - Epoch : 10 epochs(best at 7 epoch) - Random Seed : 42 - Learning Rate : 5e-5 - Warm up Ratio : 0.1 - Batch Size : 32 ## 4. BLEU Score - Jit + AI HUB(+아래아 문자) Dataset : 67.6 --- ### CREDIT - 주형준 : [email protected] - 강가람 : [email protected] - 고광연 : [email protected] - 김수연 : [email protected] - 이원경 : [email protected] - 조성은 : [email protected]
Tititun/consumer_super
Tititun
2022-05-16T04:46:12Z
3
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-05-15T15:31:47Z
--- license: mit tags: - generated_from_trainer model-index: - name: consumer_super results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # consumer_super This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu102 - Datasets 2.2.1 - Tokenizers 0.12.1
tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData
tbosse
2022-05-16T01:21:42Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-05-15T14:21:40Z
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-finetuned-subj_preTrained_with_noisyData results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-german-cased-finetuned-subj_preTrained_with_noisyData 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: 0.0192 - Precision: 0.9203 - Recall: 0.8703 - F1: 0.8946 - Accuracy: 0.9939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 245 | 0.0258 | 0.9172 | 0.7990 | 0.8540 | 0.9918 | | No log | 2.0 | 490 | 0.0192 | 0.9203 | 0.8703 | 0.8946 | 0.9939 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
nttoanh/t5vi-finetuned-en-to-vi
nttoanh
2022-05-15T22:20:38Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:mt_eng_vietnamese", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-15T17:03:36Z
--- tags: - generated_from_trainer datasets: - mt_eng_vietnamese metrics: - bleu model-index: - name: t5vi-finetuned-en-to-vi results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: mt_eng_vietnamese type: mt_eng_vietnamese args: iwslt2015-en-vi metrics: - name: Bleu type: bleu value: 13.547 --- <!-- 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. --> # t5vi-finetuned-en-to-vi This model is a fine-tuned version of [imthanhlv/t5vi](https://huggingface.co/imthanhlv/t5vi) on the mt_eng_vietnamese dataset. It achieves the following results on the evaluation set: - Loss: 1.3827 - Bleu: 13.547 - Gen Len: 17.3719 ## 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: 20 - eval_batch_size: 20 - 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 | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.8026 | 1.0 | 6666 | 1.5907 | 10.9756 | 17.3231 | | 1.6217 | 2.0 | 13332 | 1.4635 | 12.375 | 17.3444 | | 1.5087 | 3.0 | 19998 | 1.4131 | 13.1828 | 17.3924 | | 1.4446 | 4.0 | 26664 | 1.3915 | 13.5217 | 17.3617 | | 1.4076 | 5.0 | 33330 | 1.3827 | 13.547 | 17.3719 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
ali-issa/FYP_ARABIC
ali-issa
2022-05-15T19:44:11Z
14
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-05-15T14:10:23Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-arabic-gpu-colab-similar-to-german-bigger-warm-up results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-arabic-gpu-colab-similar-to-german-bigger-warm-up This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6370 - Wer: 0.4146 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - 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: 5000 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.4958 | 2.83 | 400 | 3.4822 | 1.0 | | 3.2281 | 5.67 | 800 | 2.9404 | 1.0 | | 2.942 | 8.51 | 1200 | 2.8690 | 1.0 | | 2.6346 | 11.35 | 1600 | 1.5452 | 0.9994 | | 1.3472 | 14.18 | 2000 | 0.8261 | 0.6853 | | 0.8972 | 17.02 | 2400 | 0.6812 | 0.5737 | | 0.6924 | 19.85 | 2800 | 0.6552 | 0.5291 | | 0.5687 | 22.69 | 3200 | 0.6108 | 0.4909 | | 0.4734 | 25.53 | 3600 | 0.5877 | 0.4674 | | 0.4029 | 28.37 | 4000 | 0.6204 | 0.4662 | | 0.3483 | 31.2 | 4400 | 0.5932 | 0.4451 | | 0.307 | 34.04 | 4800 | 0.6445 | 0.4392 | | 0.2722 | 36.88 | 5200 | 0.6126 | 0.4292 | | 0.2247 | 39.71 | 5600 | 0.6370 | 0.4146 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
subhasisj/zh-TAPT-MLM-MiniLM
subhasisj
2022-05-15T19:26:22Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-05-09T20:04:18Z
--- tags: - generated_from_trainer model-index: - name: zh-TAPT-MLM-MiniLM results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zh-TAPT-MLM-MiniLM This model is a fine-tuned version of [subhasisj/MiniLMv2-qa-encoder](https://huggingface.co/subhasisj/MiniLMv2-qa-encoder) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
KhariotnovKK/luna_lender_v1
KhariotnovKK
2022-05-15T18:37:37Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-06T08:33:31Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 260.20 +/- 20.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
SebastianS/bert-finetuned-squad
SebastianS
2022-05-15T16:19:22Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-15T14:39:46Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
huggingtweets/dclblogger-loopifyyy
huggingtweets
2022-05-15T15:32:50Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-05-15T15:28:31Z
--- language: en thumbnail: http://www.huggingtweets.com/dclblogger-loopifyyy/1652628765621/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1472740175130230784/L7Xcs7wJ_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1480550067564163078/D90SnyUa_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Matty & Loopify 🧙‍♂️</div> <div style="text-align: center; font-size: 14px;">@dclblogger-loopifyyy</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Matty & Loopify 🧙‍♂️. | Data | Matty | Loopify 🧙‍♂️ | | --- | --- | --- | | Tweets downloaded | 3250 | 3250 | | Retweets | 62 | 117 | | Short tweets | 494 | 867 | | Tweets kept | 2694 | 2266 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1pq5pxck/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dclblogger-loopifyyy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/as5uacn5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/as5uacn5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dclblogger-loopifyyy') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
traxes/repos
traxes
2022-05-15T15:03:59Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T15:03:31Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -130.18 +/- 34.56 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
umbertospazio/1500000_PPO-LunarLander-v2
umbertospazio
2022-05-15T15:03:24Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T15:02:54Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 283.46 +/- 17.55 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
umbertospazio/Test_PPO-LunarLander-v2
umbertospazio
2022-05-15T15:02:42Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T14:28:21Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 278.51 +/- 23.01 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
Zohar/distilgpt2-finetuned-negative-restaurant-reviews-clean
Zohar
2022-05-15T14:12:08Z
11
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-05-15T11:47:04Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-negative-restaurant-reviews-clean results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-negative-restaurant-reviews-clean This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5187 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.6841 | 1.0 | 3105 | 3.5793 | | 3.6184 | 2.0 | 6210 | 3.5313 | | 3.5943 | 3.0 | 9315 | 3.5187 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.11.0
WhatIsThisSignupForm/ppo-LunarLander-v2
WhatIsThisSignupForm
2022-05-15T12:50:36Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T12:46:36Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 174.04 +/- 57.75 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
dragonSwing/audify
dragonSwing
2022-05-15T12:49:48Z
14
0
transformers
[ "transformers", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
null
2022-04-17T06:58:48Z
--- license: cc-by-nc-sa-4.0 ---
ipvikas/rare-puppers
ipvikas
2022-05-15T12:47:13Z
61
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-05-01T16:51:17Z
--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9552238583564758 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
FollishBoi/dqn-MountainCar-v0-try2
FollishBoi
2022-05-15T12:00:52Z
2
0
stable-baselines3
[ "stable-baselines3", "MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T12:00:30Z
--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -100.70 +/- 7.47 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 --- # **DQN** Agent playing **MountainCar-v0** This is a trained model of a **DQN** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
FollishBoi/dqn-MountainCar-v0-try1
FollishBoi
2022-05-15T12:00:10Z
4
0
stable-baselines3
[ "stable-baselines3", "MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T11:59:45Z
--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -102.50 +/- 5.73 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 --- # **DQN** Agent playing **MountainCar-v0** This is a trained model of a **DQN** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
anas-awadalla/splinter-base-finetuned-squad
anas-awadalla
2022-05-15T11:49:58Z
4
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-15T10:55:15Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-base-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-base-finetuned-squad This model is a fine-tuned version of [tau/splinter-base-qass](https://huggingface.co/tau/splinter-base-qass) 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
mubikan/xlm-roberta-base-finetuned-panx-de
mubikan
2022-05-15T11:48:08Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-05-14T15:57:44Z
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8588964027959312 --- <!-- 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: 0.1383 - F1: 0.8589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2631 | 1.0 | 525 | 0.1596 | 0.8218 | | 0.1296 | 2.0 | 1050 | 0.1353 | 0.8479 | | 0.0821 | 3.0 | 1575 | 0.1383 | 0.8589 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
harikp20/hkp24
harikp20
2022-05-15T11:34:27Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-15T08:30:36Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: hkp24 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hkp24 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1619 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.2249 | 1.0 | 5533 | 1.1675 | | 0.961 | 2.0 | 11066 | 1.1376 | | 0.7581 | 3.0 | 16599 | 1.1619 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
unfinity/PPO-LunarLander-v2
unfinity
2022-05-15T10:52:13Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T10:33:12Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 262.60 +/- 17.02 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
anas-awadalla/splinter-large-finetuned-squad
anas-awadalla
2022-05-15T10:51:43Z
27
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-15T08:20:49Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-large-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-large-finetuned-squad This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
ZaynSu99/weibo_senti_cls
ZaynSu99
2022-05-15T10:46:21Z
0
0
null
[ "license:afl-3.0", "region:us" ]
null
2022-05-15T10:31:02Z
--- license: afl-3.0 --- this model is for Weibo comment sentiment analysis
nandezgarcia/roberta-base-bne-finetuned-recores
nandezgarcia
2022-05-15T10:24:41Z
12
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
multiple-choice
2022-05-15T07:43:23Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-recores results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-recores This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1113 - Accuracy: 0.4601 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5294 | 1.0 | 1047 | 1.4094 | 0.4242 | | 0.6886 | 2.0 | 2094 | 2.1629 | 0.4545 | | 0.0779 | 3.0 | 3141 | 2.3083 | 0.4545 | | 0.0103 | 4.0 | 4188 | 3.0327 | 0.4628 | | 0.0019 | 5.0 | 5235 | 3.1113 | 0.4601 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.1+cu102 - Datasets 2.2.1 - Tokenizers 0.12.1
atsanda/ppo-LunarLander-v2
atsanda
2022-05-15T09:28:07Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T09:27:35Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 241.67 +/- 9.99 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
Metformin/BART_medFineTune
Metformin
2022-05-15T09:11:06Z
3
0
transformers
[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-15T05:39:34Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Metformin/BART_medFineTune results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Metformin/BART_medFineTune This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7982 - Validation Loss: 0.9953 - Epoch: 29 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 7820, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1563 | 1.3468 | 0 | | 1.4157 | 1.2090 | 1 | | 1.2579 | 1.1387 | 2 | | 1.1819 | 1.0888 | 3 | | 1.1438 | 1.0848 | 4 | | 1.0629 | 1.0512 | 5 | | 1.0163 | 1.0454 | 6 | | 0.9801 | 1.0248 | 7 | | 0.9530 | 1.0171 | 8 | | 0.9262 | 1.0108 | 9 | | 0.9124 | 1.0116 | 10 | | 0.8853 | 1.0043 | 11 | | 0.8658 | 1.0023 | 12 | | 0.8511 | 0.9987 | 13 | | 0.8394 | 0.9988 | 14 | | 0.8298 | 0.9994 | 15 | | 0.8175 | 0.9985 | 16 | | 0.8105 | 0.9936 | 17 | | 0.8033 | 0.9974 | 18 | | 0.8012 | 0.9948 | 19 | | 0.7997 | 0.9948 | 20 | | 0.7970 | 0.9957 | 21 | | 0.7956 | 0.9958 | 22 | | 0.8002 | 0.9954 | 23 | | 0.7951 | 0.9957 | 24 | | 0.7994 | 0.9948 | 25 | | 0.7964 | 0.9958 | 26 | | 0.7948 | 0.9957 | 27 | | 0.7979 | 0.9956 | 28 | | 0.7982 | 0.9953 | 29 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.3 - Datasets 2.0.0 - Tokenizers 0.12.1
esh/ppo-LunarLander-v2
esh
2022-05-15T09:01:54Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-09T16:40:01Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 266.69 +/- 23.44 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
pujaburman30/autotrain-hi_ner_xlmr-869827677
pujaburman30
2022-05-15T09:00:47Z
4
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "token-classification", "autotrain", "unk", "dataset:pujaburman30/autotrain-data-hi_ner_xlmr", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-05-15T08:56:46Z
--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - pujaburman30/autotrain-data-hi_ner_xlmr co2_eq_emissions: 4.365496441173981 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 869827677 - CO2 Emissions (in grams): 4.365496441173981 ## Validation Metrics - Loss: 0.894961416721344 - Accuracy: 0.7411180773249739 - Precision: 0.590625 - Recall: 0.5080645161290323 - F1: 0.546242774566474 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/pujaburman30/autotrain-hi_ner_xlmr-869827677 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("pujaburman30/autotrain-hi_ner_xlmr-869827677", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("pujaburman30/autotrain-hi_ner_xlmr-869827677", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
anas-awadalla/roberta-large-houlsby-few-shot-k-512-finetuned-squad-seed-2
anas-awadalla
2022-05-15T07:40:11Z
0
0
null
[ "generated_from_trainer", "dataset:squad", "license:mit", "region:us" ]
null
2022-05-15T05:02:33Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-houlsby-few-shot-k-512-finetuned-squad-seed-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-houlsby-few-shot-k-512-finetuned-squad-seed-2 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: 0.0001 - train_batch_size: 12 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
meln1k/ppo-CarRacing-v0-v1
meln1k
2022-05-15T07:33:43Z
3
0
stable-baselines3
[ "stable-baselines3", "CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T07:32:54Z
--- library_name: stable-baselines3 tags: - CarRacing-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 800.67 +/- 46.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CarRacing-v0 type: CarRacing-v0 --- # **PPO** Agent playing **CarRacing-v0** This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
meln1k/ppo-CarRacing-v0
meln1k
2022-05-15T07:31:25Z
11
2
stable-baselines3
[ "stable-baselines3", "CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-05-15T07:19:11Z
--- library_name: stable-baselines3 tags: - CarRacing-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 840.32 +/- 21.17 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CarRacing-v0 type: CarRacing-v0 --- # **PPO** Agent playing **CarRacing-v0** This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
anas-awadalla/roberta-large-houlsby-few-shot-k-512-finetuned-squad-seed-0
anas-awadalla
2022-05-15T07:30:26Z
0
0
null
[ "generated_from_trainer", "dataset:squad", "license:mit", "region:us" ]
null
2022-05-15T04:52:31Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-houlsby-few-shot-k-512-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-houlsby-few-shot-k-512-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: 0.0001 - 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 - num_epochs: 20.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-houlsby-few-shot-k-256-finetuned-squad-seed-0
anas-awadalla
2022-05-15T07:06:17Z
0
0
null
[ "generated_from_trainer", "dataset:squad", "license:mit", "region:us" ]
null
2022-05-15T04:38:07Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-houlsby-few-shot-k-256-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-houlsby-few-shot-k-256-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: 0.0001 - 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 - num_epochs: 20.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-houlsby-few-shot-k-128-finetuned-squad-seed-0
anas-awadalla
2022-05-15T06:45:01Z
0
0
null
[ "generated_from_trainer", "dataset:squad", "license:mit", "region:us" ]
null
2022-05-15T03:10:02Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-houlsby-few-shot-k-128-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-houlsby-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: 0.0001 - 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: 400 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
ahmeddbahaa/mbart-large-50-finetuned-persian
ahmeddbahaa
2022-05-15T04:01:56Z
18
0
transformers
[ "transformers", "pytorch", "mbart", "text2text-generation", "summarization", "persian", "MBart50", "Abstractive Summarization", "generated_from_trainer", "dataset:xlsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-05-14T13:40:15Z
--- tags: - summarization - persian - MBart50 - Abstractive Summarization - generated_from_trainer datasets: - xlsum model-index: - name: mbart-large-50-finetuned-persian results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbart-large-50-finetuned-persian This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 4.1932 - Rouge-1: 26.11 - Rouge-2: 8.11 - Rouge-l: 21.09 - Gen Len: 37.29 - Bertscore: 71.08 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 5.5612 | 1.0 | 1476 | 4.5015 | 17.07 | 3.14 | 13.54 | 47.49 | 66.83 | | 4.3049 | 2.0 | 2952 | 4.1055 | 22.63 | 5.89 | 18.03 | 40.43 | 69.23 | | 3.8154 | 3.0 | 4428 | 3.9822 | 24.57 | 7.15 | 19.74 | 37.35 | 70.36 | | 3.3401 | 4.0 | 5904 | 4.0088 | 25.84 | 7.96 | 20.95 | 37.56 | 70.83 | | 2.8879 | 5.0 | 7380 | 4.1932 | 26.24 | 8.26 | 21.23 | 37.78 | 71.05 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
bkh6722/bach-arb
bkh6722
2022-05-15T02:34:26Z
30
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-05-07T21:50:59Z
<!-- 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. --> # bach-arb This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9404 - Wer: 0.6130 ## 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: 115 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 27.8653 | 7.14 | 100 | 3.1369 | 1.0 | | 2.5975 | 14.28 | 200 | 2.1223 | 0.9976 | | 1.2001 | 21.41 | 300 | 1.7455 | 0.8774 | | 0.5938 | 28.55 | 400 | 1.8534 | 0.7981 | | 0.4001 | 35.69 | 500 | 2.3318 | 0.7740 | | 0.2895 | 42.83 | 600 | 2.2214 | 0.7163 | | 0.1853 | 49.97 | 700 | 2.4841 | 0.7043 | | 0.1318 | 57.14 | 800 | 2.9749 | 0.7139 | | 0.1067 | 64.28 | 900 | 2.4759 | 0.7115 | | 0.0635 | 71.41 | 1000 | 2.6708 | 0.6635 | | 0.0515 | 78.55 | 1100 | 3.0593 | 0.6923 | | 0.0455 | 85.69 | 1200 | 2.9637 | 0.6587 | | 0.0329 | 92.83 | 1300 | 2.9837 | 0.6346 | | 0.0232 | 99.97 | 1400 | 2.9361 | 0.6178 | | 0.021 | 107.14 | 1500 | 2.9221 | 0.6010 | | 0.0193 | 114.28 | 1600 | 2.9404 | 0.6130 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
menglingbei/t5-small-finetuned-xsum
menglingbei
2022-05-15T02:03:19Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-15T01:59:39Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum model-index: - name: t5-small-finetuned-xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
anas-awadalla/splinter-large-few-shot-k-512-finetuned-squad-seed-4
anas-awadalla
2022-05-15T00:58:56Z
4
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-15T00:45:41Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-large-few-shot-k-512-finetuned-squad-seed-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-large-few-shot-k-512-finetuned-squad-seed-4 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-few-shot-k-1024-finetuned-squad-seed-4
anas-awadalla
2022-05-14T23:53:15Z
9
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T23:32:52Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-few-shot-k-1024-finetuned-squad-seed-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-few-shot-k-1024-finetuned-squad-seed-4 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: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/splinter-large-few-shot-k-1024-finetuned-squad-seed-2
anas-awadalla
2022-05-14T23:31:40Z
4
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T23:11:15Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-large-few-shot-k-1024-finetuned-squad-seed-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-large-few-shot-k-1024-finetuned-squad-seed-2 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-few-shot-k-1024-finetuned-squad-seed-2
anas-awadalla
2022-05-14T23:31:40Z
3
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T23:11:05Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-few-shot-k-1024-finetuned-squad-seed-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-few-shot-k-1024-finetuned-squad-seed-2 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: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
Metformin/T5model_medFineTune
Metformin
2022-05-14T23:15:48Z
5
0
transformers
[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-14T10:11:14Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Metformin/T5model_medFineTune results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Metformin/T5model_medFineTune This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.0442 - Validation Loss: 6.1005 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 7820, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 42.6321 | 28.0647 | 0 | | 31.2672 | 21.0068 | 1 | | 24.8310 | 16.6186 | 2 | | 20.5368 | 13.8025 | 3 | | 17.3796 | 11.7180 | 4 | | 15.0329 | 10.0404 | 5 | | 13.0886 | 8.6286 | 6 | | 11.5235 | 7.5594 | 7 | | 10.1123 | 6.8079 | 8 | | 9.0442 | 6.1005 | 9 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.3 - Datasets 2.0.0 - Tokenizers 0.12.1
anas-awadalla/splinter-large-few-shot-k-1024-finetuned-squad-seed-0
anas-awadalla
2022-05-14T23:09:42Z
4
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T22:49:18Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-large-few-shot-k-1024-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-large-few-shot-k-1024-finetuned-squad-seed-0 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-few-shot-k-512-finetuned-squad-seed-0
anas-awadalla
2022-05-14T22:17:30Z
8
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T22:04:23Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-few-shot-k-512-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-few-shot-k-512-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 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-few-shot-k-256-finetuned-squad-seed-4
anas-awadalla
2022-05-14T22:02:44Z
3
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T21:52:47Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-few-shot-k-256-finetuned-squad-seed-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-few-shot-k-256-finetuned-squad-seed-4 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: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-few-shot-k-256-finetuned-squad-seed-2
anas-awadalla
2022-05-14T21:51:44Z
8
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T21:41:56Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-few-shot-k-256-finetuned-squad-seed-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-few-shot-k-256-finetuned-squad-seed-2 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: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-0
anas-awadalla
2022-05-14T21:40:52Z
3
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T21:30:12Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-large-few-shot-k-256-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-large-few-shot-k-256-finetuned-squad-seed-0 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/roberta-large-few-shot-k-256-finetuned-squad-seed-0
anas-awadalla
2022-05-14T21:40:29Z
4
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T21:30:14Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-few-shot-k-256-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-few-shot-k-256-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 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
anas-awadalla/splinter-large-few-shot-k-128-finetuned-squad-seed-4
anas-awadalla
2022-05-14T21:28:38Z
3
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T21:16:06Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-large-few-shot-k-128-finetuned-squad-seed-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-large-few-shot-k-128-finetuned-squad-seed-4 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 4 - 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
anas-awadalla/roberta-large-few-shot-k-128-finetuned-squad-seed-4
anas-awadalla
2022-05-14T21:28:38Z
3
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T21:10:06Z
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-large-few-shot-k-128-finetuned-squad-seed-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-few-shot-k-128-finetuned-squad-seed-4 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: 4 - 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
anas-awadalla/splinter-large-few-shot-k-128-finetuned-squad-seed-2
anas-awadalla
2022-05-14T21:14:58Z
4
0
transformers
[ "transformers", "pytorch", "splinter", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-05-14T21:02:01Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: splinter-large-few-shot-k-128-finetuned-squad-seed-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # splinter-large-few-shot-k-128-finetuned-squad-seed-2 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6