license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to gener... | b9144676e8c465278f177525e0f4e9c4 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note ... | 2c9e2acab99923bd89b9f7fbf24877b6 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-400k') model = BertModel.from_pretrained("multiberts-seed-4-400k") text = "Replace me by any text you'd like.... | b30137ee912b0c75bd0b5e9d067b38cd |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snip... | 8f7aabea8dd380816450317eb00edad1 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). | 4bcefc062d45d28e6dd27f4349332ee9 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the o... | 4c490b75c72b569a8709cba84b95c1cf |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,... | d267f3859be98191bffc7918a6b1e494 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and ... | 47741996fcf345d3254bb05d29f3e68b |
mit | [] | false | Isabell Schulte - PVIII - 12tiles - 3000steps - Style on Stable Diffusion This is the `<isabell-schulte-p8-style-12tiles-3000s>` 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/... | 0a97d675aeb7cbb2b732345b8f347227 |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-finetuned-FER2013-0.003-CKPlus This model is a fine-tuned version of [Celal11/resnet-50-finetuned-FER2013-0.003](https://huggingface.co/Celal11/resnet-50-finetuned-FER2013-0.003) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0614 - Accuracy: 0.9848 | 1d803dc13de7f0bff412203034cc9115 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 7a218e801530781860d7f58a0a9ebb19 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6689 | 0.97 | 27 | 0.1123 | 0.9797 | | 0.2929 | 1.97 | 54 | 0.0614 | 0.9848 | | a6783a5c2e3fb3e2183f75ec3237b36e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8024 - Matthews Correlation: 0.5275 | 50f40b68cb75eed645106da79cbe7377 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5261 | 1.0 | 535 | 0.5320 | 0.4152 | | 0.3482 | 2.0 | 1070 | 0.4960 | 0.5049 | | 0.2... | 00557f6311c7d6817eedbc211600b7f4 |
mit | [] | false | Model description It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This model is uncased: it does not make a difference between indonesia and Indonesia. This is one of several other language models that have been pre-trained with indonesian datasets. More... | 0afe373ed929ceb60199d7d617eefc39 |
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='cahya/gpt2-small-indonesian-522M') >>> set_see... | 9ae529cd2b50624ef7011769d91fdc13 |
mit | [] | false | Training data This model was pre-trained with 522MB of indonesian Wikipedia. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens. | 36413f808c0b5b676f1e0ae07a9a9211 |
mit | ['torch'] | false | GPT-2 Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language... | bc30e174b5642d3fc74630921c074f3f |
mit | ['torch'] | false | Model description This is the **SMALL** version compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925). The compression was executed on Bulgarian text from [OSCAR](https://oscar-corpus.com/post/oscar-2019/), [Chitanka](https://chitanka.info/) and [Wikipedia](https://bg.wikipedia.org/). | 481dc65e5ac5e3d24aba628c581496df |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import AutoModel, AutoTokenizer >>> >>> model_id = "rmihaylov/gpt2-small-theseus-bg" >>> tokenizer = AutoTokenizer.from_pretrained(model_id) >>> model = AutoModel.from_pretrained(model_id, trust_remote_code=True) >>> >>> input_ids =... | 888955e82a4533c0417be0491ad940c0 |
mit | ['torch'] | false | out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend... | 36848b22212c0b64f18cf0ef2c488428 |
mit | [] | false | Usage ```python import torch from transformers import AutoTokenizer, AutoModel, AutoModelForSeq2SeqLM model_name = "vblagoje/bart_lfqa" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name... | 635e3023660300c40a06066e8065458f |
mit | [] | false | given the question above suppose these documents below were found in some document store documents = ["when the skin is completely wet. The body continuously loses water by...", "at greater pressures. There is an ambiguity, however, as to the meaning of the terms 'heating' and 'cooling'...", ... | f8b0f37f60e92d8bf19d37e8f17fbfbb |
mit | [] | false | concatenate question and support documents into BART input conditioned_doc = "<P> " + " <P> ".join([d for d in documents]) query_and_docs = "question: {} context: {}".format(query, conditioned_doc) model_input = tokenizer(query_and_docs, truncation=True, padding=True, return_tensors="pt") generated_answers_encoded =... | 767698ff6461a8270bb0ce752778bc97 |
mit | [] | false | below is the abstractive answer generated by the model ["When you heat water to room temperature, it loses heat to the air around it. When you cool it down, it gains heat back from the air, which is why it feels colder than the air surrounding it. It's the same reason why you feel cold when you turn on a fan. The air ... | b9116c379ab94fd1160f97f4a27711b2 |
apache-2.0 | ['translation'] | false | opus-mt-tiv-fr * source languages: tiv * target languages: fr * OPUS readme: [tiv-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tiv-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | b21281303ce5c999846d290421e915e0 |
apache-2.0 | ['generated_from_trainer'] | false | test_ner3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pv_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.2983 - Precision: 0.6698 - Recall: 0.6499 - F1: 0.6597 - Accuracy: 0.9607 | 4b2e194fc3d51cc75fa24dc81a1f2fb5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | ceacd0c39e7b98f9ecbba4eff4aff286 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1106 | 1.0 | 1813 | 0.1128 | 0.6050 | 0.5949 | 0.5999 | 0.9565 | | 0.0705 | 2.0 ... | c651dbae466fb3898e5baab5ba68ff16 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-ar-9 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 86.4276 - Wer: 0.1947 | ad1719f7e56c8013bbeabed502bb8176 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 06acfac459cf990b45c5d1d89b62b7d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 6312.2087 | 4.71 | 400 | 616.6482 | 1.0 | | 1928.3641 | 9.41 | 800 | 135.8992 | 0.6373 | | 502.0017 | 14.12 | 1200 | 84.4729 | ... | c7af1c4469b150180b91d4851858cbbd |
apache-2.0 | ['generated_from_keras_callback'] | false | bertbaseuncasedny This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3901 - Train End Logits Accuracy: 0.8823 - Train Start Logits Accuracy: 0.8513 - Validation Loss: 1.2123... | f880f335da4d5f41874519ad1eadb9a9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 29508, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 2213e5ca32396b8dd2ab538cd5219065 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 69bae67a40c62c734886786d6b8e2ecc |
mit | ['generated_from_keras_callback'] | false | YSKartal/bert-base-turkish-cased-turkish_offensive_trained_model This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on [offenseval2020_tr](https://huggingface.co/datasets/offenseval2020_tr) dataset. It achieves the following results on the evalu... | 563670d307ec5fcf3d489180f47a889c |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7936, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | a3a42a80614eb45d278a5592c17505fd |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train F1 | Epoch | |:----------:|:---------------:|:--------:|:-----:| | 0.3003 | 0.2664 | 0.6971 | 0 | | 0.1866 | 0.3018 | 0.6990 | 1 | | 0.0860 | 0.3803 | 0.7032 | 2 | | 0.0365 | 0.4846 | 0.6993 ... | 65afb6f91f36f0b3e61babd332e6e907 |
mit | ['conversational'] | false | I fine-tuned DialoGPT-small model on "The Big Bang Theory" TV Series dataset from Kaggle (https://www.kaggle.com/mitramir5/the-big-bang-theory-series-transcript) ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("vijayv500/DialoGPT-small-B... | 422c31f2334ab91a4e220e3f8bb32430 |
mit | ['conversational'] | false | generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, tempera... | 2fbfdb0e6b92303a178609a7799a1ddc |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_mrpc This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5133 - Accuracy: 0.6740 - F1: 0.7772 - Combined Score: 0.7256 | 1bd1fefbdc1a4b42fb636b5da2c1042f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6228 | 1.0 | 29 | 0.5556 | 0.6838 | 0.8122 | 0.7480 | | 0.611 | 2.0 | 58 | 0.55... | 36703e037f0a63fa25c700ba1b3441e0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.1258 - Accuracy: 0.9793 | a82b546f3205a55dfc843bf7179535c3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | e75e23022551c6b949620d398010d569 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1561 | 1.0 | 399 | 1.1127 | 0.6643 | | 0.4803 | 2.0 | 798 | 0.3547 | 0.9687 | | 0.2855 | 3.0 | 1197 | 0.1663 | 0.... | b9b6191150bf29820153f04a17bca198 |
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: 0.2147 - Accuracy: 0.927 - F1: 0.9270 | 05fe14f93fe7a299d5e5c14dc49576d2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8181 | 1.0 | 250 | 0.3036 | 0.9085 | 0.9064 | | 0.2443 | 2.0 | 500 | 0.2147 | 0.927 | 0.9270 | | 31b2bde93be5576cea1bbbea108d30ed |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/ehr-bert-base-uncased-cchs-wordlevel This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7374 - Epoch: 9 | 27760fb7a68e1b17c609ed854c298c49 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 - block_size: 512 - batch_size: ... | 79bb8d818e644cbc1f24a1a26d1ef877 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.8857 | 0 | | 3.7525 | 1 | | 3.7505 | 2 | | 3.7493 | 3 | | 3.7412 | 4 | | 3.7432 | 5 | | 3.7428 | 6 | | 3.7409 | 7 | | 3.7394 | 8 | | 3.7374 | 9 | | 193207095b33ae648670748568bc2cc9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium Greek El Greco This model is a fine-tuned version of [emilios/whisper-medium-el-n2](https://huggingface.co/emilios/whisper-medium-el-n2) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 0.5669 - Wer: 9.8997 | 377549fc489b6b0822661e054abbf7c7 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - train... | 2092fc8b2d15d2b9ce1cdfa8c6e7cab5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:-------:| | 0.0014 | 58.82 | 1000 | 0.4951 | 10.3640 | | 0.0006 | 117.65 | 2000 | 0.5181 | 10.2805 | | 0.0007 | 175.82 | 3000 | 0.5317 ... | 88fc008c75ec2fe92ad746bec656a71b |
mit | [] | false | Model miniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency. Since miniALBERT is a compact model, it is trained using a layer-to-layer distillation technique, using the BioBERT-v1.1 model as the teache... | 219d2524688299ce0ff7ea8659e5c07d |
mit | [] | false | Usage Since miniALBERT uses a unique architecture it can not be loaded using ts.AutoModel for now. To load the model, first, clone the miniALBERT GitHub project, using the below code: ```bash git clone https://github.com/nlpie-research/MiniALBERT.git ``` Then use the ```sys.path.append``` to add the miniALBERT files t... | ae1219ab8f0bba5a61ef8da6f830a99a |
mit | [] | false | For Sequence Classification use the below code model = MiniAlbertForTokenClassification.from_pretrained("nlpie/bio-miniALBERT-128") ``` In addition, For efficient fine-tuning using the pre-trained bottleneck adapters use the below code: ```Python model.trainAdaptersOnly() ``` | e05b83598c217da88acac30b1f62bd78 |
mit | [] | false | Citation If you use the model, please cite our paper: ``` @article{nouriborji2022minialbert, title={MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers}, author={Nouriborji, Mohammadmahdi and Rohanian, Omid and Kouchaki, Samaneh and Clifton, David A}, journal={arXiv preprint arXiv:2210... | ef5261261120eddef3f2428c181a5a3f |
apache-2.0 | ['generated_from_trainer'] | false | tst-translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 1.5889 - Bleu: 13.3161 - Gen Len: 42.493 | 73d2101cba3027687bfb78489f9c24d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 7c108e89e3e66a03d64af9e016238481 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1700k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1700k 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... | 310affc43f0d3fb75cd658d57ced6d60 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1700k'] | 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_0-step_1700k') model = TFBertModel.from_pretrained("google/multib... | b916af07fd5b4f27f0ea5592cdb2206c |
apache-2.0 | ['generated_from_trainer'] | false | reddit-bert-text4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4763 | 362483e376c991605ee5c7c534cc1f98 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1071 | 1.0 | 978 | 2.6170 | | 2.6788 | 2.0 | 1956 | 2.5332 | | 2.6112 | 3.0 | 2934 | 2.4844 | | 5d2221684269eb7be76fa5e31dec7029 |
apache-2.0 | ['translation'] | false | rus-dan * source group: Russian * target group: Danish * OPUS readme: [rus-dan](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-dan/README.md) * model: transformer-align * source language(s): rus * target language(s): dan * model: transformer-align * pre-processing: normalization + Sente... | 87c92db053fa4bdea111609261e31446 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: rus-dan - source_languages: rus - target_languages: dan - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-dan/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ru', 'da'] - src_constituents: {'rus'} - tgt_const... | 2bfc6824480c5aef9ae93422993e3154 |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | 45d7631c320db6fa7432691377302708 |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-NL 6B) was pre-trained on [the Pile](https://github.com/EleutherAI/the-pile), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai/). Parts of the dataset include code data. | 08c593c09e08e696f3ffc2c2bb00f23c |
bsd-3-clause | [] | false | Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. | 84f5fabc0b4b9ce38ac85087c038e41c |
bsd-3-clause | [] | false | Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at **program synthesis**, that is, generating executable code give... | ff976d481e0487572be0de152e90aaeb |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-nl") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-nl") text = "def... | 3d965a15294cc54b7f2767b142ea609b |
bsd-3-clause | [] | false | BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ``` | a464443f1f63e7f62119169133753280 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | S2T2-Wav2Vec2-CoVoST2-EN-AR-ST `s2t-wav2vec2-large-en-ar` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST). The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in [Fairs... | 3f8e367fcfbb1f4d13cb8e4ec7f9001b |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | Model description S2T2 is a transformer-based seq2seq (speech encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a pretrained [Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html) as the encoder and a transformer-based decoder. The ... | 1d87215a362cd110c58b50c838fc2fc8 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | Intended uses & limitations This model can be used for end-to-end English speech to Arabic text translation. See the [model hub](https://huggingface.co/models?filter=speech2text2) to look for other S2T2 checkpoints. | dad9261dddfcb7b220d9ef0ea065717a |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline ```python from datasets import load_dataset from transformers import pipeline librispe... | 63861afffd93e88f1e6eaadfba9644c9 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | Evaluation results CoVoST-V2 test results for en-ar (BLEU score): **20.2** For more information, please have a look at the [official paper](https://arxiv.org/pdf/2104.06678.pdf) - especially row 10 of Table 2. | 7c2791df74b12612b99f96859ec04390 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2104-06678, author = {Changhan Wang and Anne Wu and Juan Miguel Pino and Alexei Baevski and Michael Auli and Alexis Conneau}, title = {Large-Scale Self- and Se... | 06a93f8b008378ab48786b72ae02e986 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1460 - Accuracy: 0.75 | 1ade7c1ad67fd91441dc17cfff566cc0 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum-wei2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4131 - Rouge1: 29.2287 - Rouge2: 8.4073 - Rougel: 23.0934 - Rougelsum: 23.0954 - Gen Len: 18.8236 | 1550c45ebaa0acdfa0daae88e6488246 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | a70e8172405df0f7d12b339b6f4daa9c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.633 | 1.0 | 17004 | 2.4131 | 29.2287 | 8.4073 | 23.0934 | 23.0954 | 18... | a44236a878c05fd6eaf04827c1efe2e9 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-bert-sst2-distilled-model This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.2592 - Accuracy: 0.8383 | a2698146bd23ca09f912d2c21f8c3e2a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 - mixed_precision_training: Native AMP | 900fbd00dac3d4ca0587f7b99ff5abd9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5303 | 1.0 | 4210 | 1.2542 | 0.8222 | | 0.4503 | 2.0 | 8420 | 1.1260 | 0.8211 | | 0.3689 | 3.0 | 12630 | 1.2325 ... | 6af071aef8572ba86b852c771df5dd65 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_cooking-roberta-large-v1-2-4 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transforme... | 74c6b5d2155140c70790e049942fdad2 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords ... | 261073ed26c1159dee00153ba29ccd26 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-base-finetuned-ar-wikilingua This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.6790 - Rouge-1: 19.46 - Rouge-2: 6.82 - Rouge-l: 17.57 - Gen Len: 18.83 - Bertscore: 70.18 | f28774fcf07ee1452cacb3bce3d5f443 |
apache-2.0 | ['summarization', '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: 250 - num_epochs: 8 - label_smoothing_facto... | 22c37df36f5db8e462bdec62694c074c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 4.9783 | 1.0 | 5111 | 4.0107 | 15.8 | 4.65 | 14.18 | 18.98 | 6... | 76e2d68718be3b65f848fb6aa888f8ba |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-finetuned-summarization-cnn This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.0105 - Rouge1: 24.4825 - Rouge2: 9.1573 - Rougel: 19.7135 - Rougelsum: 22.2551 | 2958c9ea6236fd978c129a21eb1a07f8 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | b19d72faea8c7f12a136571d8aceab62 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 2.0389 | 1.0 | 718 | 2.0150 | 24.4413 | 9.1782 | 19.7202 | 22.2225 | | 1.9497 | 2.0 |... | 9cf3af3b08011b3090375a05d0bf8774 |
apache-2.0 | ['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: 1 | 24c4da64e66f5e66fc959f01d97d3bbb |
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 | 63 | 1.3966 | 24.7113 | 17.3364 | 22.3967 | 24.026 | 19... | ac4fa0caea21dae9d95c60555595c682 |
other | ['generated_from_trainer'] | false | NLP_Opt350M This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3806 | 876a11e2b12486b84da70f829bca6349 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.453 | 1.0 | 849 | 3.3589 | | 2.9744 | 2.0 | 1698 | 3.3594 | | 2.7146 | 3.0 | 2547 | 3.3806 | | 3a000a9360b98705d93c6307937eedd7 |
apache-2.0 | ['generated_from_trainer'] | false | oldData_BERT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 | f82393229666cdd5a661a9c6aa08e119 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | 2e298f49bb0c3fad8e771cbf0f3efce8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2348 | 1.0 | 1125 | 1.0185 | | 1.0082 | 2.0 | 2250 | 0.7174 | | 0.699 | 3.0 | 3375 | 0.3657 | | 0.45 | 4.0 | 4500 | 0.1880 ... | 8001bc2ec650ed95913d34ef1297d637 |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss w... | a48e6965a351a3c277462c3dc76ccb7c |
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