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 a... | 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... | 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 ... | 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 = tokeniz... | 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 ... | 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/multibe... | 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-ja... | 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 fo... | 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 hig... | 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 | | War... | 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 s... | 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). BioMegatr... | 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.... | 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 examp... | 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-L... | 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 you... | 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 ... | 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 |... | 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... | 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 nu... | 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/... | 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)... | 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 ... | 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.... | 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 releasi... | 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_sche... | 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 | 0.736... | 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](ht... | 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://... | 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: 56.2... | 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 | ... | 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 |... | 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 t... | 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 rele... | 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 ... | 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 - n... | 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://... | 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） | | *... | 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 |... | 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 ... | 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... | 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 R... | 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) mod... | 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', pretraine... | 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... | 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': 'Polynomia... | 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-p... | 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_... | 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 o... | 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, Ka... | 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 ... | 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 ... | 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... | 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_fact... | 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 |... | 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 (`... | 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): inp... | 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 ... | 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... | 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... | 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://... | e2c2a97be9abe626a5d2139d89ad9cb0 |
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