license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.01_pretrainedTrue_epochs1 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 2.8746 | d405052705df58831aa16afe6589851d |
mit | ['lao-roberta-base'] | false | Lao RoBERTa Base Lao RoBERTa Base is a masked language model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. It was trained on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset, specifically the `deduplicated_lo` subset. The model was trained from scratch and achieved an evaluation loss of 1.4556 and an evaluation perplexity of 4.287. This model was trained using HuggingFace's PyTorch framework and the training script found [here](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py). All training was done on a TPUv3-8, provided by the [TPU Research Cloud](https://sites.research.google/trc/about/) program. You can view the detailed training results in the [Training metrics](https://huggingface.co/w11wo/lao-roberta-base/tensorboard) tab, logged via Tensorboard. | 320f90cd04eb95bcfcd283791d1ab97b |
mit | ['lao-roberta-base'] | false | params | Arch. | Training/Validation data (text) | | ------------------ | ------- | ------- | ------------------------------------ | | `lao-roberta-base` | 124M | RoBERTa | OSCAR-2109 `deduplicated_lo` Dataset | | 5f4051e53ef94d8aa1c8e956c851af5f |
mit | ['lao-roberta-base'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - distributed_type: tpu - num_devices: 8 - total_train_batch_size: 1024 - total_eval_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30.0 | fa4f4e76b53aef0c45432f82c2c3b7cb |
mit | ['lao-roberta-base'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | | :-----------: | :---: | :--: | :-------------: | | No log | 1.0 | 216 | 5.8586 | | No log | 2.0 | 432 | 5.5095 | | 6.688 | 3.0 | 648 | 5.3976 | | 6.688 | 4.0 | 864 | 5.3562 | | 5.3629 | 5.0 | 1080 | 5.2912 | | 5.3629 | 6.0 | 1296 | 5.2385 | | 5.22 | 7.0 | 1512 | 5.1955 | | 5.22 | 8.0 | 1728 | 5.1785 | | 5.22 | 9.0 | 1944 | 5.1327 | | 5.1248 | 10.0 | 2160 | 5.1243 | | 5.1248 | 11.0 | 2376 | 5.0889 | | 5.0591 | 12.0 | 2592 | 5.0732 | | 5.0591 | 13.0 | 2808 | 5.0417 | | 5.0094 | 14.0 | 3024 | 5.0388 | | 5.0094 | 15.0 | 3240 | 4.9299 | | 5.0094 | 16.0 | 3456 | 4.2991 | | 4.7527 | 17.0 | 3672 | 3.6541 | | 4.7527 | 18.0 | 3888 | 2.7826 | | 3.4431 | 19.0 | 4104 | 2.2796 | | 3.4431 | 20.0 | 4320 | 2.0213 | | 2.2803 | 21.0 | 4536 | 1.8809 | | 2.2803 | 22.0 | 4752 | 1.7615 | | 2.2803 | 23.0 | 4968 | 1.6925 | | 1.8601 | 24.0 | 5184 | 1.6205 | | 1.8601 | 25.0 | 5400 | 1.5751 | | 1.6697 | 26.0 | 5616 | 1.5391 | | 1.6697 | 27.0 | 5832 | 1.5200 | | 1.5655 | 28.0 | 6048 | 1.4866 | | 1.5655 | 29.0 | 6264 | 1.4656 | | 1.5655 | 30.0 | 6480 | 1.4627 | | b40a4a3e4b6fa76e7f50de8c326a8fb8 |
mit | ['lao-roberta-base'] | false | As Masked Language Model ```python from transformers import pipeline pretrained_name = "w11wo/lao-roberta-base" prompt = "REPLACE WITH MASKED PROMPT" fill_mask = pipeline( "fill-mask", model=pretrained_name, tokenizer=pretrained_name ) fill_mask(prompt) ``` | 90e07c6e54ea7144428ec50cc5cd5a26 |
mit | ['lao-roberta-base'] | false | Feature Extraction in PyTorch ```python from transformers import RobertaModel, RobertaTokenizerFast pretrained_name = "w11wo/lao-roberta-base" model = RobertaModel.from_pretrained(pretrained_name) tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name) prompt = "ສະບາຍດີຊາວໂລກ." encoded_input = tokenizer(prompt, return_tensors='pt') output = model(**encoded_input) ``` | a08164f8fbc57e088b4dc942b12fca21 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_120k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 120k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 011c87b8f1467aa425c0177a9d1611e3 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_120k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_120k') model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_120k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_120k') model = BertModel.from_pretrained("google/multiberts-seed_3-step_120k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 8e54cd76ba3c2c972bf8b2668b77f072 |
cc-by-sa-4.0 | ['transformers', 'sentence-similarity', 'feature-extraction', 'sentence-transformers'] | false | summary model name: `pkshatech/simcse-ja-bert-base-clcmlp` This is a Japanese [SimCSE](https://arxiv.org/abs/2104.08821) model. You can easily extract sentence embedding representations from Japanese sentences. This model is based on [`cl-tohoku/bert-base-japanese-v2`](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) and trained on [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) dataset, which is a Japanese natural language inference dataset. | 0f4850b0b3ce65c1783cf9f5d525291a |
cc-by-sa-4.0 | ['transformers', 'sentence-similarity', 'feature-extraction', 'sentence-transformers'] | false | Usage (Sentence-Transformers) You can use this model easily with [sentence-transformers](https://www.SBERT.net). You need [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://pypi.org/project/unidic-lite/) for tokenization. Please install sentence-transformers, fugashi, and unidic-lite with pip as follows: ``` pip install -U fugashi[unidic-lite] sentence-transformers ``` You can load the model and convert sentences to dense vectors as follows: ```python from sentence_transformers import SentenceTransformer sentences = [ "PKSHA Technologyは機械学習/深層学習技術に関わるアルゴリズムソリューションを展開している。", "この深層学習モデルはPKSHA Technologyによって学習され、公開された。", "広目天は、仏教における四天王の一尊であり、サンスクリット語の「種々の眼をした者」を名前の由来とする。", ] model = SentenceTransformer('pkshatech/simcse-ja-bert-base-clcmlp') embeddings = model.encode(sentences) print(embeddings) ``` Since the loss function used during training is cosine similarity, we recommend using cosine similarity for downstream tasks. | 0b22ec0667e249843cd26926da85f917 |
cc-by-sa-4.0 | ['transformers', 'sentence-similarity', 'feature-extraction', 'sentence-transformers'] | false | Tokenization We use the same tokenizer as `tohoku/bert-base-japanese-v2`. Please see the [README of `tohoku/bert-base-japanese-v2`](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) for details. | 245d37decb2bf82e01c15ff62d8a9be7 |
cc-by-sa-4.0 | ['transformers', 'sentence-similarity', 'feature-extraction', 'sentence-transformers'] | false | Training We set `tohoku/bert-base-japanese-v2` as the initial value and trained it on the train set of [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88). We trained 20 epochs and published the checkpoint of the model with the highest Spearman's correlation coefficient on the validation set [^1] of the train set of [JSTS](https://github.com/yahoojapan/JGLUE) | b122c7abd959a15d7f0b941a9d438fed |
cc-by-sa-4.0 | ['transformers', 'sentence-similarity', 'feature-extraction', 'sentence-transformers'] | false | Training Parameters | Parameter | Value | | --- | --- | |pooling_strategy | [CLS] -> single fully-connected layer | | max_seq_length | 128 | | with hard negative | true | | temperature of contrastive loss | 0.05 | | Batch size | 200 | | Learning rate | 1e-5 | | Weight decay | 0.01 | | Max gradient norm | 1.0 | | Warmup steps | 2012 | | Scheduler | WarmupLinear | | Epochs | 20 | | Evaluation steps | 250 | | 6ed777ad7d73ec38ef25faea51b6433b |
cc-by-sa-4.0 | ['transformers', 'sentence-similarity', 'feature-extraction', 'sentence-transformers'] | false | Licenses This models are distributed under the terms of the Creative [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/). [^1]: When we trained this model, the test data of JGLUE was not released, so we used the dev set of JGLUE as a private evaluation data. Therefore, we selected the checkpoint on the train set of JGLUE insted of its dev set. | 568e46ae0c65029c0ec35a89ab004dcb |
cc-by-4.0 | ['language model'] | false | --> [BioMegatron](https://arxiv.org/pdf/2010.06060.pdf) is a transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model trained on top of the Megatron-LM model, adding a PubMed corpusto the Megatron-LM corpora(Wikipedia, RealNews, OpenWebText, and CC-Stories). BioMegatron follows a similar (albeit not identical) architecture as BERT and it has 345 million parameters: * 24 layers * 16 attention heads with a hidden size of 1024. More information available at [nVIDIA NGC CATALOG](https://catalog.ngc.nvidia.com/orgs/nvidia/models/biomegatron345muncased) | b72d2cf2e90591271ff82fb56e82199e |
cc-by-4.0 | ['language model'] | false | Running BioMegatron in 🤗 transformers In this implementation we have followed the commands of the [`nvidia/megatron-bert-uncased-345m`](https://huggingface.co/nvidia/megatron-bert-uncased-345m) repository to make BioMegatron available in 🤗. However, the file [`convert_megatron_bert_checkpoint.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py) needed a modification. The reason is that the Megatron model shown in [`nvidia/megatron-bert-uncased-345m`](https://huggingface.co/nvidia/megatron-bert-uncased-345m) has included head layers, while the weights of the BioMegatron model that we upload to this repository do not contain a head. We provide in the repository an alternative version of the [python script](https://huggingface.co/EMBO/BioMegatron345mUncased/blob/main/convert_biomegatron_checkpoint.py) in order to any user to cross-check the validity of the model replicated in this repository. The code below is a modification of the original [`convert_megatron_bert_checkpoint.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py). ```python import os import torch from convert_biomegatron_checkpoint import convert_megatron_checkpoint print_checkpoint_structure = True path_to_checkpoint = "/path/to/BioMegatron345mUncased/" | 3d1bfdbceaa88cbd41564f609e1697f9 |
cc-by-4.0 | ['language model'] | false | Store the config to file. output_config_file = os.path.join(path_to_checkpoint, "config.json") print(f'Saving config to "{output_config_file}"') with open(output_config_file, "w") as f: json.dump(output_config, f) | f3a95669723bbd989515abce0c1bda3b |
cc-by-4.0 | ['language model'] | false | Store the state_dict to file. output_checkpoint_file = os.path.join(path_to_checkpoint, "pytorch_model.bin") print(f'Saving checkpoint to "{output_checkpoint_file}"') torch.save(output_state_dict, output_checkpoint_file) ``` BioMegatron can be run with the standard 🤗 script for loading models. Here we show an example identical to that of [`nvidia/megatron-bert-uncased-345m`](https://huggingface.co/nvidia/megatron-bert-uncased-345m). ```python import os import torch from transformers import BertTokenizer, MegatronBertForMaskedLM, AutoModelForMaskedLM checkpoint = "EMBO/BioMegatron345mUncased" | 9a8bdb4fc0e826dbe8f14a01156cfee1 |
cc-by-4.0 | ['language model'] | false | Create inputs (from the BERT example page). input = tokenizer("The capital of France is [MASK]", return_tensors="pt").to(device) label = tokenizer("The capital of France is Paris", return_tensors="pt")["input_ids"].to(device) | 5c1c0adfa309a7768230a7d5eb103f3b |
apache-2.0 | ['generated_from_trainer'] | false | mnli This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4595 - Accuracy: 0.8230 | 58ebe5c36a264952e5d487e2966bb6a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.3 | 28ee94c335801be0301ded877758f2c5 |
apache-2.0 | ['collaborative', 'bengali', 'NER'] | false | Model description [sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) fine-tuned for NER using the bengali split of [WikiANN ](https://huggingface.co/datasets/wikiann). Named Entities predicted by the model: | Label id | Label | |:--------:|:----:| |0 |O| |1 |B-PER| |2 |I-PER| |3 |B-ORG| |4 |I-ORG| |5 |B-LOC| |6 |I-LOC| | a8e67823bb625ddf976362142fe9ecf8 |
apache-2.0 | ['collaborative', 'bengali', 'NER'] | false | How to use You can use this model directly with a pipeline for token classification: ```python from transformers import AlbertForTokenClassification, TokenClassificationPipeline, PreTrainedTokenizerFast | 6081958c33a0f4214dd18b8700e7d1ef |
apache-2.0 | ['collaborative', 'bengali', 'NER'] | false | Training data The model was initialized with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) at step 19519 and trained on the bengali split of [WikiANN ](https://huggingface.co/datasets/wikiann) | 2bde44d355cd46b2fdd4a227e40f680c |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_vp-sv_s470 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 81c3bdc0a5e8ee8b11a2f23b86545461 |
cc-by-4.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-amazon_reviews_multi-taller This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2463 - Accuracy: 0.9113 | 79cb0086f722f7943cd6362bda138500 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2474 | 1.0 | 125 | 0.2463 | 0.9113 | | ed640f205de2f8b549e2819ad097f60b |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-ft-google This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [steciuk/google](https://huggingface.co/datasets/steciuk/google) dataset. It achieves the following results on the evaluation set: - Loss: 0.3195 - Accuracy: 0.9105 - F1: 0.9174 and flowing results on the testing set: - Accuracy: 0.9096 - F1: 0.9161 | 66a56af57f5fa018967969900ac88531 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3651 | 0.37 | 196 | 0.2641 | 0.8962 | 0.9064 | | 0.2765 | 0.75 | 392 | 0.2484 | 0.9019 | 0.9099 | | 0.2349 | 1.12 | 588 | 0.2532 | 0.9133 | 0.9205 | | 0.2015 | 1.49 | 784 | 0.2692 | 0.9095 | 0.9139 | | 0.1817 | 1.86 | 980 | 0.2957 | 0.9095 | 0.9180 | | 0.1683 | 2.24 | 1176 | 0.2941 | 0.9143 | 0.9213 | | 0.1204 | 2.61 | 1372 | 0.3230 | 0.9143 | 0.9223 | | 0.1271 | 2.98 | 1568 | 0.3195 | 0.9105 | 0.9174 | | 03bee46c9f87255dae6a03f1658c841e |
cc-by-4.0 | ['espnet', 'audio', 'self-supervised-learning'] | false | `simpleoier/simpleoier_librispeech_hubert_iter1_train_ssl_torchaudiohubert_base_960h_pretrain_it1_raw` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). | e49b0d6cb138d3d4037e55bb39611e0a |
cc-by-4.0 | ['espnet', 'audio', 'self-supervised-learning'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 753f40d61813436d4e76660904d02eaed7a6649e pip install -e . cd egs2/librispeech/ssl1 ./run.sh --skip_data_prep false --skip_train true --download_model simpleoier/simpleoier_librispeech_hubert_iter1_train_ssl_torchaudiohubert_base_960h_pretrain_it1_raw ``` | b18968544101d50b47c48a8dfea10ef0 |
cc-by-4.0 | ['espnet', 'audio', 'self-supervised-learning'] | false | SSL config <details><summary>expand</summary> ``` config: conf/tuning/train_ssl_torchaudiohubert_base_960h_pretrain_it1.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/hubert_iter1_train_ssl_torchaudiohubert_base_960h_pretrain_it1_raw ngpu: 1 seed: 0 num_workers: 64 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 49251 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 250 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 45000000 valid_batch_bins: null train_shape_file: - exp/hubert_iter1_stats_raw/train/speech_shape - exp/hubert_iter1_stats_raw/train/text_shape.word valid_shape_file: - exp/hubert_iter1_stats_raw/valid/speech_shape - exp/hubert_iter1_stats_raw/valid/text_shape.word batch_type: numel valid_batch_type: null fold_length: - 80000 - 400 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_960/wav.scp - speech - sound - - dump/raw/train_960/text.km.kmeans_iter1_hubert_train_960_portion0.1 - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/text.km.kmeans_iter1_hubert_train_960_portion0.1 - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0005 scheduler: warmuplr scheduler_conf: warmup_steps: 32000 token_list: - '386' - '160' - '89' - '3' - '448' - '431' - '319' - '247' - '256' - '23' - '267' - '274' - '479' - '227' - '197' - '74' - '362' - '159' - '190' - '275' - '241' - '147' - '242' - '105' - '7' - '320' - '311' - '327' - '130' - '485' - '427' - '22' - '493' - '254' - '451' - '399' - '342' - '443' - '38' - '33' - '53' - '238' - '86' - '61' - '263' - '218' - '316' - '350' - '96' - '492' - '341' - '496' - '325' - '462' - '24' - '328' - '133' - '407' - '41' - '304' - '373' - '167' - '352' - '456' - '149' - '279' - '84' - '217' - '494' - '139' - '381' - '416' - '305' - '446' - '337' - '228' - '35' - '372' - '55' - '237' - '66' - '13' - '188' - '291' - '43' - '132' - '232' - '144' - '497' - '318' - '0' - '31' - '49' - '400' - '10' - '406' - '398' - '154' - '300' - '226' - '93' - '348' - '82' - '2' - '423' - '113' - '395' - '92' - '394' - '293' - '62' - '137' - '476' - '216' - '432' - '155' - '29' - '369' - '64' - '163' - '389' - '278' - '25' - '164' - '310' - '213' - '126' - '331' - '414' - '11' - '404' - '185' - '365' - '484' - '409' - '17' - '193' - '178' - '273' - '37' - '390' - '128' - '170' - '203' - '298' - '229' - '383' - '67' - '27' - '118' - '72' - '142' - '73' - '65' - '231' - '104' - '124' - '428' - '345' - '230' - '287' - '175' - '294' - '184' - '97' - '48' - '457' - '288' - '204' - '379' - '107' - '200' - '99' - '269' - '442' - '353' - '129' - '445' - '51' - '360' - '80' - '83' - '201' - '223' - '312' - '69' - '30' - '202' - '70' - '286' - '236' - '50' - '123' - '88' - '205' - '151' - '127' - '186' - '367' - '299' - '313' - '220' - '206' - '297' - '422' - '71' - '44' - '281' - '91' - '57' - '408' - '112' - '26' - '145' - '16' - '75' - '235' - '183' - '222' - '171' - '121' - '250' - '472' - '195' - '94' - '357' - '393' - '380' - '370' - '363' - '103' - '396' - '468' - '346' - '40' - '180' - '42' - '351' - '450' - '477' - '239' - '143' - '361' - '314' - '392' - '161' - '473' - '198' - '194' - '371' - '433' - '56' - '444' - '138' - '157' - '245' - '140' - '165' - '412' - '354' - '9' - '333' - '85' - '176' - '323' - '301' - '215' - '264' - '434' - '489' - '355' - '488' - '382' - '177' - '268' - '290' - '114' - '266' - '334' - '356' - '90' - '244' - '259' - '368' - '6' - '303' - '478' - '199' - '376' - '480' - '401' - '1' - '168' - '453' - '19' - '54' - '221' - '100' - '4' - '495' - '77' - '240' - '45' - '481' - '224' - '20' - '120' - '58' - '162' - '12' - '109' - '491' - '115' - '397' - '340' - '196' - '68' - '34' - '415' - '429' - '421' - '475' - '335' - '338' - '172' - '39' - '258' - '330' - '246' - '425' - '296' - '125' - '60' - '52' - '271' - '173' - '469' - '289' - '439' - '207' - '487' - '272' - '332' - '284' - '308' - '388' - '95' - '248' - '101' - '36' - '14' - '315' - '262' - '146' - '343' - '79' - '426' - '21' - '253' - '63' - '292' - '81' - '385' - '309' - '366' - '116' - '131' - '87' - '449' - '283' - '214' - '474' - '329' - '471' - '225' - '108' - '136' - '148' - '306' - '150' - '378' - '460' - '307' - '141' - '98' - '436' - '402' - '192' - '8' - '483' - '440' - '47' - '466' - '486' - '5' - '257' - '447' - '377' - '111' - '251' - '490' - '265' - '438' - '158' - '384' - '135' - '102' - '276' - '211' - '219' - '187' - '347' - '32' - '182' - '169' - '410' - '455' - '461' - '482' - '374' - '463' - '452' - '59' - '152' - '174' - '418' - '166' - '470' - '459' - '153' - '179' - '498' - '430' - '419' - '467' - '208' - '326' - '210' - '270' - '243' - '255' - '233' - '261' - '336' - '282' - '234' - '464' - '181' - '156' - '359' - '454' - '420' - '28' - '249' - '106' - '302' - '191' - '209' - '46' - '117' - '403' - '280' - '324' - '458' - '134' - '122' - '212' - '18' - '437' - '78' - '375' - '252' - '405' - '295' - '435' - '317' - '260' - '364' - '322' - '15' - '339' - '413' - '465' - '285' - '189' - '417' - '344' - '110' - '119' - '277' - '499' - '358' - '411' - '387' - '349' - '424' - '391' - '76' - '441' - '321' - <unk> - <sos/eos> init: null collate_fn_conf: label_downsampling: 1 pad: false rand_crop: true input_size: 1 num_classes: 500 use_preprocessor: true token_type: word bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' pred_masked_weight: 1.0 pred_nomask_weight: 0.0 loss_weights: 0.0 frontend: null frontend_conf: {} specaug: null specaug_conf: {} normalize: null normalize_conf: {} preencoder: null preencoder_conf: {} encoder: torchaudio_hubert encoder_conf: encoder_projection_dropout: 0.1 encoder_attention_dropout: 0.1 encoder_ff_interm_dropout: 0.0 encoder_dropout: 0.1 encoder_layer_drop: 0.05 model: torchaudio model_conf: {} required: - output_dir - token_list version: '202209' distributed: true ``` </details> | 8ddac8f8faa4a91deb762255fbf8e495 |
mit | ['GPT-2'] | false | Spanish GPT-2 trained on [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus) This is a Spanish GPT-2 model trained from scratch on the [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus) aka BETO's corpus with [Flax](https://github.com/google/flax) This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. | 2e0269547bbf89fe9c5c8f6d4f8b5158 |
mit | ['GPT-2'] | false | Team members - Manuel Romero ([mrm8488](https://huggingface.co/mrm8488)) - María Grandury ([mariagrandury](https://huggingface.co/)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) - Daniel Vera ([daveni](https://huggingface.co/daveni)) - Sri Lakshmi ([srisweet](https://huggingface.co/srisweet)) - José Posada ([jdposa](https://huggingface.co/jdposa)) - Santiago Hincapie ([shpotes](https://huggingface.co/shpotes)) - Jorge ([jorgealro](https://huggingface.co/jorgealro)) | 12f41b457e51ad51496498c32d6dfa6c |
mit | ['GPT-2'] | false | summary-timeline-calendar-6) - [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md) - [Community Week thread](https://discuss.huggingface.co/t/pretrain-gpt2-from-scratch-in-spanish/7086/8) | ba6a7c4744647ab9efef51f9c375168d |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-7-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 | 0579a855e3f7158d6790ae0e5b3407ed |
afl-3.0 | [] | false | Citation Information ``` @inproceedings{adelani-etal-2022-thousand, title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation", author = "Adelani, David and Alabi, Jesujoba and Fan, Angela and Kreutzer, Julia and Shen, Xiaoyu and Reid, Machel and Ruiter, Dana and Klakow, Dietrich and Nabende, Peter and Chang, Ernie and Gwadabe, Tajuddeen and Sackey, Freshia and Dossou, Bonaventure F. P. and Emezue, Chris and Leong, Colin and Beukman, Michael and Muhammad, Shamsuddeen and Jarso, Guyo and Yousuf, Oreen and Niyongabo Rubungo, Andre and Hacheme, Gilles and Wairagala, Eric Peter and Nasir, Muhammad Umair and Ajibade, Benjamin and Ajayi, Tunde and Gitau, Yvonne and Abbott, Jade and Ahmed, Mohamed and Ochieng, Millicent and Aremu, Anuoluwapo and Ogayo, Perez and Mukiibi, Jonathan and Ouoba Kabore, Fatoumata and Kalipe, Godson and Mbaye, Derguene and Tapo, Allahsera Auguste and Memdjokam Koagne, Victoire and Munkoh-Buabeng, Edwin and Wagner, Valencia and Abdulmumin, Idris and Awokoya, Ayodele and Buzaaba, Happy and Sibanda, Blessing and Bukula, Andiswa and Manthalu, Sam", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.223", doi = "10.18653/v1/2022.naacl-main.223", pages = "3053--3070", abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.", } ``` | 72d0559f8d27d239678354a735b3827e |
mit | [] | false | huang guang jian on Stable Diffusion This is the `<huang-guang-jian>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:     | 402414378236300089adf8765eb1bf57 |
mit | ['donut', 'image-to-text', 'vision'] | false | Donut (base-sized model, fine-tuned on RVL-CDIP) Donut model fine-tuned on RVL-CDIP. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team. | e9e66df2eeb8dc6b98b685c9f72234e9 |
mit | ['donut', 'image-to-text', 'vision'] | false | Intended uses & limitations This model is fine-tuned on RVL-CDIP, a document image classification dataset. We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples. | 652a5420dfef6c88cc36f832c167ab66 |
apache-2.0 | ['generated_from_trainer', 'pt'] | false | WavLM-large-CORAA-pt This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on [CORAA dataset](https://github.com/nilc-nlp/CORAA). It achieves the following results on the evaluation set: - Loss: 0.6144 - Wer: 0.3840 | b4e3dc7a3c7da86f522729c5bb73b0e7 |
apache-2.0 | ['generated_from_trainer', 'pt'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 40000 - mixed_precision_training: Native AMP | 8a48a3190ea12fb5dba58e244ad36ada |
apache-2.0 | ['generated_from_trainer', 'pt'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.04 | 1000 | 1.9230 | 0.9960 | | 5.153 | 0.08 | 2000 | 1.3733 | 0.8444 | | 5.153 | 0.13 | 3000 | 1.1992 | 0.7362 | | 1.367 | 0.17 | 4000 | 1.1289 | 0.6957 | | 1.367 | 0.21 | 5000 | 1.0357 | 0.6470 | | 1.1824 | 0.25 | 6000 | 1.0216 | 0.6201 | | 1.1824 | 0.29 | 7000 | 0.9338 | 0.6036 | | 1.097 | 0.33 | 8000 | 0.9149 | 0.5760 | | 1.097 | 0.38 | 9000 | 0.8885 | 0.5541 | | 1.0254 | 0.42 | 10000 | 0.8678 | 0.5366 | | 1.0254 | 0.46 | 11000 | 0.8349 | 0.5323 | | 0.9782 | 0.5 | 12000 | 0.8230 | 0.5155 | | 0.9782 | 0.54 | 13000 | 0.8245 | 0.5049 | | 0.9448 | 0.59 | 14000 | 0.7802 | 0.4990 | | 0.9448 | 0.63 | 15000 | 0.7650 | 0.4900 | | 0.9092 | 0.67 | 16000 | 0.7665 | 0.4796 | | 0.9092 | 0.71 | 17000 | 0.7568 | 0.4795 | | 0.8764 | 0.75 | 18000 | 0.7403 | 0.4615 | | 0.8764 | 0.8 | 19000 | 0.7219 | 0.4644 | | 0.8498 | 0.84 | 20000 | 0.7180 | 0.4502 | | 0.8498 | 0.88 | 21000 | 0.7017 | 0.4436 | | 0.8278 | 0.92 | 22000 | 0.6992 | 0.4395 | | 0.8278 | 0.96 | 23000 | 0.7021 | 0.4329 | | 0.8077 | 1.0 | 24000 | 0.6892 | 0.4265 | | 0.8077 | 1.05 | 25000 | 0.6940 | 0.4248 | | 0.7486 | 1.09 | 26000 | 0.6767 | 0.4202 | | 0.7486 | 1.13 | 27000 | 0.6734 | 0.4150 | | 0.7459 | 1.17 | 28000 | 0.6650 | 0.4152 | | 0.7459 | 1.21 | 29000 | 0.6559 | 0.4078 | | 0.7304 | 1.26 | 30000 | 0.6536 | 0.4088 | | 0.7304 | 1.3 | 31000 | 0.6537 | 0.4025 | | 0.7183 | 1.34 | 32000 | 0.6462 | 0.4008 | | 0.7183 | 1.38 | 33000 | 0.6381 | 0.3973 | | 0.7059 | 1.42 | 34000 | 0.6266 | 0.3930 | | 0.7059 | 1.46 | 35000 | 0.6280 | 0.3921 | | 0.6983 | 1.51 | 36000 | 0.6248 | 0.3897 | | 0.6983 | 1.55 | 37000 | 0.6275 | 0.3872 | | 0.6892 | 1.59 | 38000 | 0.6199 | 0.3852 | | 0.6892 | 1.63 | 39000 | 0.6180 | 0.3842 | | 0.691 | 1.67 | 40000 | 0.6144 | 0.3840 | | a91e4159bde1dc7826cff844047ecf00 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Galician (gl) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 01:24:37.165 | 0f459592d4ac385d1e796e9d88e75082 |
apache-2.0 | ['translation'] | false | opus-mt-tn-fr * source languages: tn * target languages: fr * OPUS readme: [tn-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tn-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tn-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tn-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tn-fr/opus-2020-01-16.eval.txt) | 276556fa3acfef4315447cf1d7251d00 |
apache-2.0 | ['generated_from_keras_callback'] | false | Question Answering with Hugging Face Transformers and Keras 🤗❤️ This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on SQuAD dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9300 - Validation Loss: 1.1437 - Epoch: 1 | b5091834bbb4fbba63bfe21f6603b080 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: mixed_float16 | 97540e5a398a21cfd2e839a691770363 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv-earlystopping This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8793 - Rouge1: 56.2055 - Rouge2: 41.9231 - Rougel: 45.0616 - Rougelsum: 54.6643 - Gen Len: 142.0 | 5811ab2475798d5c414c15d8628889a2 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP | ec57d428866ca0fcba609050c3f84ec0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 0.31 | 125 | 1.2057 | 50.9339 | 30.6777 | 32.6396 | 47.9592 | 141.3519 | | No log | 0.63 | 250 | 1.0933 | 52.0728 | 31.2361 | 32.8214 | 48.9776 | 141.9815 | | No log | 0.94 | 375 | 0.9685 | 51.6847 | 32.1578 | 34.1933 | 48.8808 | 141.5556 | | 1.1594 | 1.26 | 500 | 0.9725 | 50.5131 | 30.6043 | 32.1861 | 47.4346 | 142.0 | | 1.1594 | 1.57 | 625 | 0.9342 | 52.228 | 32.2073 | 33.797 | 49.2395 | 142.0 | | 1.1594 | 1.88 | 750 | 0.8715 | 52.2 | 33.6602 | 36.1303 | 49.7138 | 141.6481 | | 1.1594 | 2.2 | 875 | 0.8334 | 53.116 | 33.9871 | 35.9641 | 50.7658 | 141.8889 | | 0.6845 | 2.51 | 1000 | 0.8241 | 52.2612 | 32.8025 | 35.27 | 49.5694 | 142.0 | | 0.6845 | 2.83 | 1125 | 0.7986 | 54.1803 | 35.0019 | 37.4582 | 51.4577 | 142.0 | | 0.6845 | 3.14 | 1250 | 0.8532 | 52.1328 | 32.6086 | 34.7455 | 49.6219 | 141.7037 | | 0.6845 | 3.45 | 1375 | 0.8319 | 51.9614 | 32.8544 | 35.3269 | 49.3279 | 141.7593 | | 0.4488 | 3.77 | 1500 | 0.8033 | 53.1404 | 34.6086 | 37.5482 | 50.7414 | 142.0 | | 0.4488 | 4.08 | 1625 | 0.8322 | 53.1736 | 34.8662 | 37.7514 | 51.0601 | 142.0 | | 0.4488 | 4.4 | 1750 | 0.7985 | 51.8251 | 32.9457 | 36.4164 | 49.55 | 142.0 | | 0.4488 | 4.71 | 1875 | 0.8049 | 54.3423 | 36.6293 | 39.1316 | 52.2706 | 141.8148 | | 0.3017 | 5.03 | 2000 | 0.8148 | 53.0698 | 35.2569 | 38.406 | 50.9346 | 141.7778 | | 0.3017 | 5.34 | 2125 | 0.8153 | 53.4479 | 35.1525 | 37.8071 | 51.3731 | 141.0741 | | 0.3017 | 5.65 | 2250 | 0.8009 | 52.5517 | 34.8287 | 37.999 | 50.2889 | 141.6111 | | 0.3017 | 5.97 | 2375 | 0.7509 | 54.2725 | 37.4164 | 40.516 | 52.1379 | 142.0 | | 0.2052 | 6.28 | 2500 | 0.8019 | 54.622 | 36.4776 | 39.9306 | 52.5069 | 142.0 | | 0.2052 | 6.6 | 2625 | 0.8176 | 55.4796 | 38.4502 | 41.5523 | 53.5211 | 142.0 | | 0.2052 | 6.91 | 2750 | 0.7956 | 55.4906 | 37.9064 | 40.845 | 53.107 | 141.9815 | | 0.2052 | 7.22 | 2875 | 0.7966 | 54.5177 | 37.3399 | 40.7678 | 52.4241 | 142.0 | | 0.1465 | 7.54 | 3000 | 0.8311 | 54.3473 | 37.0659 | 40.2507 | 52.372 | 142.0 | | 0.1465 | 7.85 | 3125 | 0.8227 | 53.9245 | 36.4695 | 39.1205 | 51.9416 | 141.8889 | | 0.1465 | 8.17 | 3250 | 0.7947 | 54.766 | 38.4275 | 41.2293 | 52.9075 | 142.0 | | 0.1465 | 8.48 | 3375 | 0.7954 | 54.5305 | 37.6934 | 40.6804 | 52.5884 | 141.9444 | | 0.115 | 8.79 | 3500 | 0.8433 | 54.7962 | 37.9373 | 41.3906 | 52.3778 | 142.0 | | 0.115 | 9.11 | 3625 | 0.8416 | 56.59 | 41.2271 | 44.4207 | 54.7199 | 142.0 | | 0.115 | 9.42 | 3750 | 0.8164 | 55.1903 | 39.0588 | 41.4908 | 53.4897 | 142.0 | | 0.115 | 9.74 | 3875 | 0.8363 | 55.2894 | 39.3598 | 42.1138 | 53.831 | 141.8889 | | 0.0912 | 10.05 | 4000 | 0.8850 | 55.7705 | 40.4924 | 43.1048 | 54.254 | 142.0 | | 0.0912 | 10.36 | 4125 | 0.8268 | 56.1664 | 40.641 | 42.798 | 54.0001 | 141.9259 | | 0.0912 | 10.68 | 4250 | 0.8564 | 55.4701 | 39.4949 | 42.2559 | 53.4486 | 141.8889 | | 0.0912 | 10.99 | 4375 | 0.8557 | 56.0849 | 41.2861 | 45.8277 | 54.5999 | 141.6667 | | 0.0707 | 11.31 | 4500 | 0.8432 | 54.9496 | 39.3006 | 42.0025 | 53.3854 | 142.0 | | 0.0707 | 11.62 | 4625 | 0.8377 | 54.2438 | 37.6959 | 40.4637 | 52.3088 | 142.0 | | 0.0707 | 11.93 | 4750 | 0.8794 | 55.9488 | 40.5401 | 43.7347 | 54.1282 | 142.0 | | 0.0707 | 12.25 | 4875 | 0.8563 | 57.8762 | 43.366 | 46.6757 | 56.6985 | 142.0 | | 0.0604 | 12.56 | 5000 | 0.8835 | 54.8926 | 39.3755 | 42.384 | 53.2687 | 141.6481 | | 0.0604 | 12.88 | 5125 | 0.8570 | 55.6656 | 39.849 | 42.1455 | 54.352 | 142.0 | | 0.0604 | 13.19 | 5250 | 0.8539 | 57.1549 | 41.901 | 45.153 | 55.213 | 142.0 | | 0.0604 | 13.51 | 5375 | 0.8847 | 56.3279 | 40.9269 | 43.416 | 54.7242 | 142.0 | | 0.051 | 13.82 | 5500 | 0.8795 | 56.8982 | 42.3333 | 45.2669 | 55.1034 | 142.0 | | 0.051 | 14.13 | 5625 | 0.8751 | 55.3173 | 40.2853 | 43.2479 | 53.7236 | 142.0 | | 0.051 | 14.45 | 5750 | 0.8799 | 56.1678 | 41.0862 | 43.8581 | 54.6316 | 142.0 | | 0.051 | 14.76 | 5875 | 0.8678 | 57.3539 | 43.0473 | 44.8511 | 55.6474 | 142.0 | | 0.0467 | 15.08 | 6000 | 0.8945 | 56.1939 | 41.985 | 45.0266 | 54.8139 | 142.0 | | 0.0467 | 15.39 | 6125 | 0.9245 | 56.2071 | 41.5265 | 44.3228 | 54.5042 | 141.4074 | | 0.0467 | 15.7 | 6250 | 0.8793 | 56.2055 | 41.9231 | 45.0616 | 54.6643 | 142.0 | | 39dd2eac0445d03ced413683b1c1b7ef |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0656 - Precision: 0.9308 - Recall: 0.9482 - F1: 0.9394 - Accuracy: 0.9858 | 9cf9ebf2a69d41f6e71fee14c6baa100 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0877 | 1.0 | 1756 | 0.0811 | 0.9077 | 0.9273 | 0.9174 | 0.9804 | | 0.0341 | 2.0 | 3512 | 0.0642 | 0.9234 | 0.9448 | 0.9340 | 0.9854 | | 0.0187 | 3.0 | 5268 | 0.0656 | 0.9308 | 0.9482 | 0.9394 | 0.9858 | | 6b22e5bf773d36757d34e775bfaf5138 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_age_teens-0_sixties-10_s423 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | ab7b45af3988462def363ae672f17907 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE (Deep-Narrow version) T5-Efficient-BASE is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | da744b56a96c26e708d44904b223ae08 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base** - is of model type **Base** with no variations. It has **222.93** million parameters and thus requires *ca.* **891.73 MB** of memory in full precision (*fp32*) or **445.86 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | d487ad7590dbb7c193649353842e0c86 |
apache-2.0 | ['generated_from_trainer'] | false | koelectra-base-86371428 This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the custom_squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.6169 | 43a3d9b8c4599cfc3f6f1a779c9d00be |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 128 - eval_batch_size: 128 - seed: 30 - gradient_accumulation_steps: 8 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 2 - mixed_precision_training: Native AMP | 6f6ea9cdb01077341b25d3e3d94d1801 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.94 | 10 | 1.8078 | | No log | 1.94 | 20 | 1.6169 | | 57cfe84354ef738c4860d5963fb2fe58 |
apache-2.0 | ['translation'] | false | opus-mt-lu-sv * source languages: lu * target languages: sv * OPUS readme: [lu-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lu-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lu-sv/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lu-sv/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lu-sv/opus-2020-01-09.eval.txt) | 05e5dbfe9fb6ba1d06343d059d72c1a5 |
apache-2.0 | ['generated_from_trainer'] | false | chinese-address-ner This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.1080 - Precision: 0.9664 - Recall: 0.9774 - F1: 0.9719 - Accuracy: 0.9758 | 941e0aad728336322b7c726b3a2a0a7b |
apache-2.0 | ['generated_from_trainer'] | false | Model description 输入一串地址中文信息，比如快递单：`北京市海淀区西北旺东路10号院(马连洼街道西北旺社区东北方向)`，按照行政级别（总有 7 级）抽取地址信息，返回每个 token 的类别。具体类别含义表示如下： | 返回类别 | BIO 体系 | 解释 | | ----------- | -------- | ---------------------- | | **LABEL_0** | O | 忽略信息 | | **LABEL_1** | B-A1 | 第一级地址（头） | | **LABEL_2** | I-A1 | 第一级地址（其余部分） | | ... | ... | ... | More information needed | d4043b708d109eaf5fe2e68da63e38ce |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | dcb0cbdd7ab5a780d0aaa66e4f3d52bb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 2.5055 | 1.0 | 7 | 1.6719 | 0.1977 | 0.2604 | 0.2248 | 0.5649 | | 1.837 | 2.0 | 14 | 1.0719 | 0.4676 | 0.6 | 0.5256 | 0.7421 | | 1.0661 | 3.0 | 21 | 0.7306 | 0.6266 | 0.7472 | 0.6816 | 0.8106 | | 0.8373 | 4.0 | 28 | 0.5197 | 0.6456 | 0.8113 | 0.7191 | 0.8614 | | 0.522 | 5.0 | 35 | 0.3830 | 0.7667 | 0.8679 | 0.8142 | 0.9001 | | 0.4295 | 6.0 | 42 | 0.3104 | 0.8138 | 0.8906 | 0.8505 | 0.9178 | | 0.3483 | 7.0 | 49 | 0.2453 | 0.8462 | 0.9132 | 0.8784 | 0.9404 | | 0.2471 | 8.0 | 56 | 0.2081 | 0.8403 | 0.9132 | 0.8752 | 0.9428 | | 0.2299 | 9.0 | 63 | 0.1979 | 0.8419 | 0.9245 | 0.8813 | 0.9420 | | 0.1761 | 10.0 | 70 | 0.1823 | 0.8830 | 0.9396 | 0.9104 | 0.9500 | | 0.1434 | 11.0 | 77 | 0.1480 | 0.9036 | 0.9547 | 0.9284 | 0.9629 | | 0.134 | 12.0 | 84 | 0.1341 | 0.9173 | 0.9623 | 0.9392 | 0.9678 | | 0.128 | 13.0 | 91 | 0.1365 | 0.9375 | 0.9623 | 0.9497 | 0.9694 | | 0.0824 | 14.0 | 98 | 0.1159 | 0.9557 | 0.9774 | 0.9664 | 0.9734 | | 0.0744 | 15.0 | 105 | 0.1092 | 0.9591 | 0.9736 | 0.9663 | 0.9766 | | 0.0569 | 16.0 | 112 | 0.1117 | 0.9556 | 0.9736 | 0.9645 | 0.9742 | | 0.0559 | 17.0 | 119 | 0.1040 | 0.9628 | 0.9774 | 0.9700 | 0.9790 | | 0.0456 | 18.0 | 126 | 0.1052 | 0.9593 | 0.9774 | 0.9682 | 0.9782 | | 0.0405 | 19.0 | 133 | 0.1133 | 0.9590 | 0.9698 | 0.9644 | 0.9718 | | 0.0315 | 20.0 | 140 | 0.1060 | 0.9591 | 0.9736 | 0.9663 | 0.9750 | | 0.0262 | 21.0 | 147 | 0.1087 | 0.9554 | 0.9698 | 0.9625 | 0.9718 | | 0.0338 | 22.0 | 154 | 0.1183 | 0.9625 | 0.9698 | 0.9662 | 0.9726 | | 0.0225 | 23.0 | 161 | 0.1080 | 0.9664 | 0.9774 | 0.9719 | 0.9758 | | 0.028 | 24.0 | 168 | 0.1057 | 0.9591 | 0.9736 | 0.9663 | 0.9742 | | 0.0202 | 25.0 | 175 | 0.1062 | 0.9628 | 0.9774 | 0.9700 | 0.9766 | | 0.0168 | 26.0 | 182 | 0.1097 | 0.9664 | 0.9774 | 0.9719 | 0.9758 | | 0.0173 | 27.0 | 189 | 0.1093 | 0.9628 | 0.9774 | 0.9700 | 0.9774 | | 0.0151 | 28.0 | 196 | 0.1162 | 0.9628 | 0.9774 | 0.9700 | 0.9766 | | 0.0135 | 29.0 | 203 | 0.1126 | 0.9483 | 0.9698 | 0.9590 | 0.9758 | | 0.0179 | 30.0 | 210 | 0.1100 | 0.9449 | 0.9698 | 0.9572 | 0.9774 | | 0.0161 | 31.0 | 217 | 0.1098 | 0.9449 | 0.9698 | 0.9572 | 0.9766 | | 0.0158 | 32.0 | 224 | 0.1191 | 0.9483 | 0.9698 | 0.9590 | 0.9734 | | 0.0151 | 33.0 | 231 | 0.1058 | 0.9483 | 0.9698 | 0.9590 | 0.9750 | | 0.0121 | 34.0 | 238 | 0.0990 | 0.9593 | 0.9774 | 0.9682 | 0.9790 | | 0.0092 | 35.0 | 245 | 0.1128 | 0.9519 | 0.9698 | 0.9607 | 0.9774 | | 0.0097 | 36.0 | 252 | 0.1181 | 0.9627 | 0.9736 | 0.9681 | 0.9766 | | 0.0118 | 37.0 | 259 | 0.1185 | 0.9591 | 0.9736 | 0.9663 | 0.9782 | | 0.0118 | 38.0 | 266 | 0.1021 | 0.9557 | 0.9774 | 0.9664 | 0.9823 | | 0.0099 | 39.0 | 273 | 0.1000 | 0.9559 | 0.9811 | 0.9683 | 0.9815 | | 0.0102 | 40.0 | 280 | 0.1025 | 0.9559 | 0.9811 | 0.9683 | 0.9815 | | 0.0068 | 41.0 | 287 | 0.1080 | 0.9522 | 0.9774 | 0.9646 | 0.9807 | | 0.0105 | 42.0 | 294 | 0.1157 | 0.9449 | 0.9698 | 0.9572 | 0.9766 | | 0.0083 | 43.0 | 301 | 0.1207 | 0.9380 | 0.9698 | 0.9536 | 0.9766 | | 0.0077 | 44.0 | 308 | 0.1208 | 0.9483 | 0.9698 | 0.9590 | 0.9766 | | 0.0077 | 45.0 | 315 | 0.1176 | 0.9483 | 0.9698 | 0.9590 | 0.9774 | | 0.0071 | 46.0 | 322 | 0.1137 | 0.9483 | 0.9698 | 0.9590 | 0.9790 | | 0.0075 | 47.0 | 329 | 0.1144 | 0.9483 | 0.9698 | 0.9590 | 0.9782 | | 0.0084 | 48.0 | 336 | 0.1198 | 0.9483 | 0.9698 | 0.9590 | 0.9766 | | 0.0103 | 49.0 | 343 | 0.1217 | 0.9519 | 0.9698 | 0.9607 | 0.9766 | | 0.0087 | 50.0 | 350 | 0.1230 | 0.9519 | 0.9698 | 0.9607 | 0.9766 | | a9e3b11beb58b0f3b0cccfe7eeb7185c |
apache-2.0 | ['image-classification'] | false | resnet50d Implementation of ResNet proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) ``` python ResNet.resnet18() ResNet.resnet26() ResNet.resnet34() ResNet.resnet50() ResNet.resnet101() ResNet.resnet152() ResNet.resnet200() Variants (d) proposed in `Bag of Tricks for Image Classification with Convolutional Neural Networks <https://arxiv.org/pdf/1812.01187.pdf`_ ResNet.resnet26d() ResNet.resnet34d() ResNet.resnet50d() | 2e86e7bae3a0780d4ce8a5189be88a15 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2240 - Accuracy: 0.925 - F1: 0.9249 | b48bf1dbda9a6a99ad826b1ea1581698 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8487 | 1.0 | 250 | 0.3310 | 0.9045 | 0.9011 | | 0.2606 | 2.0 | 500 | 0.2240 | 0.925 | 0.9249 | | 2a66cafb6faa9b221174d5e8ce7c1951 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_rmlp_tiny_rw_256.sw_in1k A timm specific MaxViT (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. | 0bed7438215d8c18fb20c6b21a3e206f |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 29.1 - GMACs: 6.8 - Activations (M): 46.9 - Image size: 256 x 256 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Dataset:** ImageNet-1k | fa4ee95ab0007159df228b3621ad1461 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxvit_rmlp_tiny_rw_256.sw_in1k', pretrained=True) model = model.eval() | 86c5e4642189741bc10ce7a30ceac7ef |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_rmlp_tiny_rw_256.sw_in1k', pretrained=True, features_only=True, ) model = model.eval() | fb0ec709b4de77a3ee89a95f34efa0de |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_rmlp_tiny_rw_256.sw_in1k', pretrained=True, num_classes=0, | 464b7697f9cf6431b2f0c4eca4060083 |
apache-2.0 | [] | false | About An Abstractive text summarizer trained using lstm based sequence to sequence model with attention mechanisim. The attention model is used for generating each word of the summary conditioned on the input sentence. Used CNN_DailyMail dataset. | 866385ab0cd57ecd040d30b15981235e |
apache-2.0 | [] | false | Training Model Overview loss graph  encoder-decoder overview  | 9c6dee1cf46e42dd52905f228507aba6 |
mit | ['generated_from_keras_callback'] | false | ChiefTheLord/codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.7143 - Validation Loss: 2.2348 - Epoch: 0 | 01abcf8e27c8b6de79fdf526f65b474c |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1378398, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | c812613ee8d62523a8166c59192159de |
apache-2.0 | ['translation'] | false | ara-epo * source group: Arabic * target group: Esperanto * OPUS readme: [ara-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-epo/README.md) * model: transformer-align * source language(s): apc apc_Latn ara arq arq_Latn arz * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.eval.txt) | e4ec0c06c29e3c3ced43c9819ba8eb28 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ara-epo - source_languages: ara - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ar', 'eo'] - src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.test.txt - src_alpha3: ara - tgt_alpha3: epo - short_pair: ar-eo - chrF2_score: 0.376 - bleu: 18.9 - brevity_penalty: 0.948 - ref_len: 4506.0 - src_name: Arabic - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: ar - tgt_alpha2: eo - prefer_old: False - long_pair: ara-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 0358c3b05bc0ad39d7610d0bfbc10839 |
apache-2.0 | [] | false | Model description This is an [t5-base](https://huggingface.co/t5-base) model, finetuned to generate questions given a table using [WikiSQL](https://huggingface.co/datasets/wikisql) dataset. It was trained to take the SQL, answer and column header of a table as input to generate questions. For more information check our T3QA [paper](https://aclanthology.org/2021.emnlp-main.342/) from EMNLP 2021. | ef0769b0700aef362f9c5d6289923f58 |
apache-2.0 | [] | false | Usage One can use this model directly in the [PrimeQA](https://github.com/primeqa/primeqa) framework as in this example [notebook](https://github.com/primeqa/primeqa/blob/tableqg/notebooks/qg/tableqg_inference.ipynb). | 3c880ddf1634b376d2c37427ed58de9e |
apache-2.0 | [] | false | Citation ```bibtex @inproceedings{chemmengath2021topic, title={Topic Transferable Table Question Answering}, author={Chemmengath, Saneem and Kumar, Vishwajeet and Bharadwaj, Samarth and Sen, Jaydeep and Canim, Mustafa and Chakrabarti, Soumen and Gliozzo, Alfio and Sankaranarayanan, Karthik}, booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, pages={4159--4172}, year={2021} } ``` | 23ba8cce9af3821bd8197ec473eb876c |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-finetuned-vi-infovqa This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.4878 | 607685c99cd2a497ec8744fa560eccbe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.11 | 100 | 4.6256 | | No log | 0.21 | 200 | 4.4042 | | No log | 0.32 | 300 | 5.0021 | | No log | 0.43 | 400 | 4.2825 | | 4.6758 | 0.53 | 500 | 4.3886 | | 4.6758 | 0.64 | 600 | 4.2519 | | 4.6758 | 0.75 | 700 | 4.2977 | | 4.6758 | 0.85 | 800 | 3.9916 | | 4.6758 | 0.96 | 900 | 4.1650 | | 4.1715 | 1.07 | 1000 | 4.5001 | | 4.1715 | 1.17 | 1100 | 4.0898 | | 4.1715 | 1.28 | 1200 | 4.1623 | | 4.1715 | 1.39 | 1300 | 4.3271 | | 4.1715 | 1.49 | 1400 | 3.9661 | | 3.7926 | 1.6 | 1500 | 3.8727 | | 3.7926 | 1.71 | 1600 | 3.8934 | | 3.7926 | 1.81 | 1700 | 3.7262 | | 3.7926 | 1.92 | 1800 | 3.7701 | | 3.7926 | 2.03 | 1900 | 3.7653 | | 3.5041 | 2.13 | 2000 | 3.9261 | | 3.5041 | 2.24 | 2100 | 4.0915 | | 3.5041 | 2.35 | 2200 | 4.0348 | | 3.5041 | 2.45 | 2300 | 4.0212 | | 3.5041 | 2.56 | 2400 | 4.4653 | | 2.8475 | 2.67 | 2500 | 4.2959 | | 2.8475 | 2.77 | 2600 | 4.1039 | | 2.8475 | 2.88 | 2700 | 3.8037 | | 2.8475 | 2.99 | 2800 | 3.7552 | | 2.8475 | 3.09 | 2900 | 4.2476 | | 2.5488 | 3.2 | 3000 | 4.6716 | | 2.5488 | 3.3 | 3100 | 4.7058 | | 2.5488 | 3.41 | 3200 | 4.6266 | | 2.5488 | 3.52 | 3300 | 4.5697 | | 2.5488 | 3.62 | 3400 | 5.1017 | | 2.0347 | 3.73 | 3500 | 4.6254 | | 2.0347 | 3.84 | 3600 | 4.4822 | | 2.0347 | 3.94 | 3700 | 4.9413 | | 2.0347 | 4.05 | 3800 | 5.3600 | | 2.0347 | 4.16 | 3900 | 5.7323 | | 1.6566 | 4.26 | 4000 | 5.8822 | | 1.6566 | 4.37 | 4100 | 6.0173 | | 1.6566 | 4.48 | 4200 | 5.6688 | | 1.6566 | 4.58 | 4300 | 6.0617 | | 1.6566 | 4.69 | 4400 | 6.6631 | | 1.3348 | 4.8 | 4500 | 6.0290 | | 1.3348 | 4.9 | 4600 | 6.2455 | | 1.3348 | 5.01 | 4700 | 6.0963 | | 1.3348 | 5.12 | 4800 | 7.0983 | | 1.3348 | 5.22 | 4900 | 7.5483 | | 1.0701 | 5.33 | 5000 | 7.7187 | | 1.0701 | 5.44 | 5100 | 7.4630 | | 1.0701 | 5.54 | 5200 | 7.1394 | | 1.0701 | 5.65 | 5300 | 7.0703 | | 1.0701 | 5.76 | 5400 | 7.5611 | | 0.9414 | 5.86 | 5500 | 7.6038 | | 0.9414 | 5.97 | 5600 | 7.4878 | | d26cd608e77693d46dc287c9d7ac3040 |
mit | [] | false | German GPT2-XL (1.5B) - trained with [BigScience's DeepSpeed-Megatron-LM code base](https://github.com/bigscience-workshop/Megatron-DeepSpeed) - word embedding initialized with [WECHSEL](https://arxiv.org/abs/2112.06598) and all other weights taken from English [gpt2-xl](https://huggingface.co/gpt2-xl) - ~ 3 days on 16xA100 GPUs (~ 80 TFLOPs / GPU) - stopped after 100k steps - 26.2B tokens - less than a single epoch on `oscar_unshuffled_deduplicated_de` (excluding validation set; original model was trained for 75 epochs on less data) - bf16 - zero stage 0 - tp/pp = 1 | 9439c75e2bb08d7ac3e6804259eed7d5 |
mit | [] | false | How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='malteos/gpt2-xl-wechsel-german') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('malteos/gpt2-xl-wechsel-german') model = GPT2Model.from_pretrained('malteos/gpt2-xl-wechsel-german') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 6ccfd9ece4276953e720f91fb8b49eab |
mit | [] | false | Evaluation | Model (size) | PPL | |---|---| | `gpt2-xl-wechsel-german` (1.5B) | **14.5** | | `gpt2-wechsel-german-ds-meg` (117M) | 26.4 | | `gpt2-wechsel-german` (117M) | 26.8 | | `gpt2` (retrained from scratch) (117M) | 27.63 | | bff63a6d8ee20545d96c13be9d7f0d2b |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-finetuned-parth 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: - Loss: 4.1122 - Rouge1: 43.9082 - Rouge2: 33.2868 - Rougel: 40.0465 - Rougelsum: 43.7776 - Gen Len: 20.0 | e2fb9ff9ff918822198cf7bc0d907e99 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - label_smoothing_factor: 0.1 | 1c57bdec1d6f3d5c23148684409e1f21 |
mit | ['generated_from_trainer'] | false | xlm-sentiment-new This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6166 - Accuracy: 0.7405 - Precision: 0.7375 - Recall: 0.7405 - F1: 0.7386 | 080a242c10787664885e32cb13da13b4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5519 | 0.7310 | 0.7266 | 0.7310 | 0.7277 | | 0.5719 | 2.0 | 592 | 0.5569 | 0.75 | 0.7562 | 0.75 | 0.7302 | | 0.5719 | 3.0 | 888 | 0.5320 | 0.7243 | 0.7269 | 0.7243 | 0.7254 | | 0.477 | 4.0 | 1184 | 0.5771 | 0.7300 | 0.7264 | 0.7300 | 0.7276 | | 0.477 | 5.0 | 1480 | 0.6051 | 0.7376 | 0.7361 | 0.7376 | 0.7368 | | 0.428 | 6.0 | 1776 | 0.6166 | 0.7405 | 0.7375 | 0.7405 | 0.7386 | | ebc80c6b97afb0268d2f07bd66c6b95d |
mit | ['russian'] | false | This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) with only some Rusian and English embeddings left. More details are given in a Russian post: https://habr.com/ru/post/581932/ The model has been fine-tuned for several tasks with sentences or short paragraphs: * translation (`translate ru-en` and `translate en-ru`) * Paraphrasing (`paraphrase`) * Filling gaps in a text (`fill`). The gaps can be denoted as `___` or `_3_`, where `3` is the approximate number of words that should be inserted. * Restoring the text from a noisy bag of words (`assemble`) * Simplification of texts (`simplify`) * Dialogue response generation (`reply` based on fiction and `answer` based on online forums) * Open-book question answering (`comprehend`) * Asking questions about a text (`ask`) * News title generation (`headline`) For each task, the task name is joined with the input text by the ` | ` separator. The model can be run with the following code: ``` | acc03abb02656e4760e00d36c7fd626e |
mit | ['russian'] | false | !pip install transformers sentencepiece import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-base-multitask") model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-base-multitask") def generate(text, **kwargs): inputs = tokenizer(text, return_tensors='pt') with torch.no_grad(): hypotheses = model.generate(**inputs, num_beams=5, **kwargs) return tokenizer.decode(hypotheses[0], skip_special_tokens=True) ``` The model can be applied to each of the pretraining tasks: ``` print(generate('translate ru-en | Каждый охотник желает знать, где сидит фазан.')) | f72c04bc0296b3870d28a86bbfe67b99 |
mit | ['russian'] | false | Each hunter wants to know, where he is. print(generate('paraphrase | Каждый охотник желает знать, где сидит фазан.', encoder_no_repeat_ngram_size=1, repetition_penalty=0.5, no_repeat_ngram_size=1)) | db2b02876748bf51d1b55c297aea84f8 |
mit | ['russian'] | false | Каждый охотник знает, что фазан сидит. print(generate('simplify | Местным продуктом-специалитетом с защищённым географическим наименованием по происхождению считается люнебургский степной барашек.', max_length=32)) | 56c9d07d3abf78be2fc2a2b5fe8374c1 |
mit | ['russian'] | false | я хочу познакомиться с девушкой!!!!!!!! print(generate("comprehend | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, " "прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо. Вопрос: откуда приехал Морган?")) | 833e3dbb7a53e538ac30aeed2a6d22f4 |
mit | ['russian'] | false | из Австралии print(generate("ask | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, " "прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо.", max_length=32)) | 58646b17b295027787702b6d248bedbc |
mit | ['russian'] | false | Что разворачивается на фоне земельного конфликта между владельцами овец и ранчеро? print(generate("headline | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, " "прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо.", max_length=32)) | 29ebe612e0557bc62654e9286ef7f5d7 |
apache-2.0 | ['generated_from_trainer'] | false | bart-model2-1510-e8 This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3655 - Rouge1: 61.3129 - Rouge2: 57.3305 - Rougel: 60.8028 - Rougelsum: 60.5111 - Gen Len: 20.0 | bfd42c47fc3d2fa44641a3f0f62e7f67 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 409 | 0.4572 | 56.7459 | 47.5708 | 54.6144 | 54.9188 | 20.0 | | 0.5704 | 2.0 | 818 | 0.4349 | 58.4751 | 50.7958 | 56.5975 | 56.941 | 20.0 | | 0.1956 | 3.0 | 1227 | 0.3952 | 61.6499 | 55.4368 | 60.157 | 60.2961 | 20.0 | | 0.1177 | 4.0 | 1636 | 0.3685 | 59.8851 | 54.1843 | 58.6443 | 58.8519 | 20.0 | | 0.0752 | 5.0 | 2045 | 0.3654 | 60.975 | 55.9124 | 60.0336 | 59.8978 | 20.0 | | 0.0752 | 6.0 | 2454 | 0.3525 | 61.268 | 55.7247 | 60.2274 | 60.1515 | 20.0 | | 0.0526 | 7.0 | 2863 | 0.3519 | 61.6626 | 57.9242 | 61.0212 | 60.8486 | 20.0 | | 0.0388 | 8.0 | 3272 | 0.3655 | 61.3129 | 57.3305 | 60.8028 | 60.5111 | 20.0 | | 409c7a9fdf68d627a796f3307e632c8d |
apache-2.0 | ['translation'] | false | opus-mt-fi-nl * source languages: fi * target languages: nl * OPUS readme: [fi-nl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-nl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-nl/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-nl/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-nl/opus-2020-02-26.eval.txt) | e2c2a97be9abe626a5d2139d89ad9cb0 |
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