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 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 2.3722 | 2.1596 | 21.6350 | 8.9453 ... | b4c9f883a868286fe35f4bcc17303496 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-qa-google-en-question_v1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1358 - Rouge1: 49.6232 - Rouge2: 26.4156 - Rougel: 46.9194 - Rougelsum: 46.8814 - Gen Len: 13.5795 | 045bb324e05ef541596ea6978ff84501 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_... | d272fc6abaae51e107eef416614ee5bc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 0.27 | 100 | 3.5967 | 43.7809 | 21.3303 | 41.6782 | 41.6869 | 12... | 5a03e0fdfd2d9a1c63003e9a1f35571f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_cola_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6180 - Matthews Correlation: 0.0 | 0a6ae74a2b825a6387b0fd03a3c8e3e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.647 | 1.0 | 34 | 0.6332 | 0.0 | | 0.6203 | 2.0 | 68 | 0.6210 | 0.0 | | 0.6... | a7aad1d2bba1b7fed77ae32d3010b9c4 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-devices-sum-ver1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2335 - Rouge1: 93.7171 - Rouge2: 73.3058 - Rougel: 93.7211 - Rougelsum: 93.689 - Gen Len: 4.7246 | f9a291ed624968dab3815a9eac83ec21 |
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 | 185 | 0.6517 | 83.2503 | 55.7516 | 83.254 | 83.2722 | 4.... | a585e0ec0713a5ed1a7fa28514743bb3 |
gpl-3.0 | ['twitter', 'masked-token-prediction', 'bertweet', 'election2020', 'politics'] | false | Citation ```bibtex @inproceedings{kawintiranon2022polibertweet, title = {PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter}, author = {Kawintiranon, Kornraphop and Singh, Lisa}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, year =... | a3a4982fcae9428659179f8a1796121b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 395bda6123ae268f991e5ef1dab887b6e677974a pip install -e . cd egs2/tamil/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/tamil_slu ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | e3b27155743054aeae442065aec2477c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Oct 3 20:59:46 EDT 2021` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.3a3` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `b41391336042a4876e30d9fe5c66afb4e4be404c` - Commit date: `Wed Sep 22 10:02:03 2021 -0400` | c6d7bab49ef9b1520dacca1c2d29d8db |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/test|80|372|70.4|22.6|7.0|3.2|32.8|56.3| |inference_asr_model_valid.acc.ave_5best/valid|80|372|70.4|22.6|7.0|3.2|32.8|56.3| | 18f1a43378e1836cce4f64026e6e779c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/test|80|3234|85.9|8.2|5.9|5.5|19.6|56.3| |inference_asr_model_valid.acc.ave_5best/valid|80|3234|85.9|8.2|5.9|5.5|19.6|56.3| | b7aa0a18c328abf2ae97153919d4e91c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_wav2vec2_xlsr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp_train_asr_wav2vec2_xlsr/asr_train_asr_wav2vec2_xlsr_raw_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_in... | cf5680518948350bc32a36ab70fe3156 |
apache-2.0 | ['generated_from_keras_callback'] | false | javilonso/classificationEsp2_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9927 - Validation Loss: 0.9926 - Epoch: 2 | fd853f5b074cfd3018ea7c834820d6a6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35916, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | f564b4653a766057258a9005d9c03bc1 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.8200 | 0.9930 | 0 | | 0.9942 | 0.9947 | 1 | | 0.9927 | 0.9926 | 2 | | e489f273374da89d9fa02346e8a345ed |
apache-2.0 | ['translation'] | false | opus-mt-iso-fi * source languages: iso * target languages: fi * OPUS readme: [iso-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/iso-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 246074c440cf4692b458ee19ce3e290d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1279 | 30b163f3b1f61053e34403622f05b979 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2189 | 1.0 | 5533 | 1.1554 | | 0.9761 | 2.0 | 11066 | 1.1279 | | e01728dfcd95583ef8d4c0a84f4d7130 |
apache-2.0 | ['translation'] | false | opus-mt-sk-en * source languages: sk * target languages: en * OPUS readme: [sk-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sk-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 204aef5c3de438443168f7887c233388 |
mit | ['gpt_neo', 'code_synthesis'] | false | GPT-Neo-125M-APPS-all > **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot** | c5eed7779847142b04b7e18028471f97 |
mit | ['gpt_neo', 'code_synthesis'] | false | Training procedure The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_apps.py). Training is done for 5 epochs using AdamW optimizer and leaner decay learning rate schedule with 800 warmup steps. To reproduce the training one ... | 2f2de0a320803fde7c82463724d3bf80 |
mit | ['gpt_neo', 'code_synthesis'] | false | How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-code-clippy-12... | e451e008e3bcc4aa965b4c9616f93e0a |
mit | ['gpt_neo', 'code_synthesis'] | false | Limitations and Biases The model is intended to be used for research purposes and comes with no guarantees of quality of generated code. The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trai... | eb4ffe18cf364faac02f8fe29b7a42cb |
apache-2.0 | ['translation'] | false | ukr-heb * source group: Ukrainian * target group: Hebrew * OPUS readme: [ukr-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-heb/README.md) * model: transformer-align * source language(s): ukr * target language(s): heb * model: transformer-align * pre-processing: normalization + Sen... | 7ede90afeb427b1eeda58bc194cc25a0 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-heb - source_languages: ukr - target_languages: heb - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-heb/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'he'] - src_constituents: {'ukr'} - tgt_const... | 5bdc71ba755ffdd94dc164561bcb8566 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-DM2000 (Deep-Narrow version) T5-Efficient-LARGE-DM2000 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* checkpo... | 8454415a7a9c0ce58b2a5193fe4f083b |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-dm2000** - is of model type **Large** with the following variations: - **dm** is **2000** It has **1475.39** million parameters and thus requires *ca.* **5901.57 MB** of memory in full precision (*fp32*) or **2950.78 MB** of memory in half pre... | 9f646572cec1da0da14a64e35de6fa21 |
cc-by-4.0 | ['translation'] | false | DeUnCaser The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text. The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this means addi... | 831a7d0ca2f2de31157aa3ae91cc4acb |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout fa1b865352475b744c37f70440de1cc6b257ba70 pip install -e . cd egs2/bn_openslr53/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/bn_openslr53 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | cf239128826af790c76158ef403e093f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Jan 31 10:53:20 EST 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `9d09bf551a9fe090973de60e15adec1de6b3d054` - Commit date: `Fri Jan 21 11:43:15 2022 -0500` | fdbaad4230a28d10a28ab79a4a91a5f6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_batch_size1_lm_lm_train_lm_bpe1000_valid.loss.ave_asr_model_valid.acc.best/sbn_test|2018|6470|74.2|21.3|4.5|2.2|28.0|48.8| | cd938def042db430bc1b20a38de5bf46 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_batch_size1_lm_lm_train_lm_bpe1000_valid.loss.ave_asr_model_valid.acc.best/sbn_test|2018|39196|89.4|4.3|6.3|1.4|12.0|48.8| | 8191bcbd306e4593550c6d1e41b4b9e2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_batch_size1_lm_lm_train_lm_bpe1000_valid.loss.ave_asr_model_valid.acc.best/sbn_test|2018|15595|77.6|12.7|9.7|1.6|24.0|48.7| | 10e12c0508333d89aa4b803375a219f8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_raw_bpe1000 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank... | 5ae797b860b6665fb8cd1941289c2f9a |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7929 | 304c2a67a31c5ac50ee80a468fc9e02a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.0892 | 1.0 | 27036 | 1.8990 | | 1.9644 | 2.0 | 54072 | 1.8040 | | 1.9174 | 3.0 | 81108 | 1.7929 | | 4c8bf3a5f9f8a9e28b5ce6ab6b653fdb |
apache-2.0 | ['g2p', 'text2text-generation'] | false | ID G2P LSTM ID G2P LSTM is a grapheme-to-phoneme model based on the [LSTM](https://doi.org/10.1162/neco.1997.9.8.1735) architecture. This model was trained from scratch on a modified [Malay/Indonesian lexicon](https://huggingface.co/datasets/bookbot/id_word2phoneme). This model was trained using the [Keras](https://... | 4ee45ec027966f1609480fe88a3d2a7b |
apache-2.0 | ['g2p', 'text2text-generation'] | false | Training Procedure <details> <summary>Model Config</summary> latent_dim: 256 num_encoder_tokens: 28 num_decoder_tokens: 32 max_encoder_seq_length: 24 max_decoder_seq_length: 25 </details> <details> <summary>Training Setting</summary> batch_size: 64 optimizer: "rmsprop" loss: "c... | 86bee037d027672c64bcdc9ef8e2b103 |
apache-2.0 | ['g2p', 'text2text-generation'] | false | How to Use <details> <summary>Tokenizers</summary> g2id = { ' ': 27, "'": 0, '-': 1, 'a': 2, 'b': 3, 'c': 4, 'd': 5, 'e': 6, 'f': 7, 'g': 8, 'h': 9, 'i': 10, 'j': 11, 'k': 12, 'l': 13, ... | 615a2eeec67e65dffa92209771b738ff |
apache-2.0 | ['g2p', 'text2text-generation'] | false | Authors ID G2P LSTM was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/), [Steven Limcorn](https://stevenlimcorn.github.io/), [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory. | 0df6260cdfb028beb2ec0bbb95ffa5ce |
mit | ['generated_from_keras_callback'] | false | nandysoham16/Paper-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3349 - Train End Logits Accuracy: 0.8854 - Train Start Logits Accurac... | 3a34e98555e29246a19cc43fbe4e56e4 |
mit | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 233d970a0d3626a0ecc9e12377745b79 |
cc-by-4.0 | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [paust/pko-t5-small](https://huggingface.co/paust/pko-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.5155 - Bleu: 0.8 - Gen Len: 19.0 | cc6d22ce6d7c47a427d2e75f1d204a3f |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 6 | 10.9861 | 0.8359 | 19.0 | | No log | 2.0 | 12 | 10.5155 | 0.8 | 19.0 | | f2e1712facd5b54b123e3637795fb8ba |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | whisper-medium-mediaspeech-cv-tr This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1813 - Wer: 9.9776 | 64922447625d642958ad85040e0e6b14 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1187 | 0.33 | 1000 | 0.2169 | 13.7678 | | 0.0579 | 1.26 | 2000 | 0.1814 | 10.8222 | | 0.0313 | 2.19 | 3000 | 0.1813 | 9.9776... | aba161636247e5835c54cd8656cd106e |
apache-2.0 | ['generated_from_keras_callback'] | false | shaun-e-j/bert-finetuned-testing This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.9966 - Epoch: 1 | a41f7322cca552d2abe9085312062f89 |
apache-2.0 | ['translation'] | false | lit-ita * source group: Lithuanian * target group: Italian * OPUS readme: [lit-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-ita/README.md) * model: transformer-align * source language(s): lit * target language(s): ita * model: transformer-align * pre-processing: normalization + S... | 61a6d90adcac2b2320f8ee3ea777b5ee |
apache-2.0 | ['translation'] | false | System Info: - hf_name: lit-ita - source_languages: lit - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['lt', 'it'] - src_constituents: {'lit'} - tgt_const... | 75777da7678aef657e5f6f302137d923 |
apache-2.0 | ['text-classification', 'emotion', 'pytorch'] | false | Model Performance Comparision on Emotion Dataset from Twitter: | Model | Accuracy | F1 Score | Test Sample per Second | | --- | --- | --- | --- | | [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 | | [Bert-base-uncased-emotion](https:/... | b24b416c79eed7ad2bbbf4b7b5b72e86 |
apache-2.0 | ['text-classification', 'emotion', 'pytorch'] | false | How to Use the model: ```python from transformers import pipeline classifier = pipeline("text-classification",model='bhadresh-savani/electra-base-emotion', return_all_scores=True) prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", ) print(prediction) """ O... | f269b404cd17c706fc306b3994d8eb76 |
apache-2.0 | ['text-classification', 'emotion', 'pytorch'] | false | Eval results ```json { 'epoch': 8.0, 'eval_accuracy': 0.9195, 'eval_f1': 0.918975455617076, 'eval_loss': 0.3486028015613556, 'eval_runtime': 4.2308, 'eval_samples_per_second': 472.726, 'eval_steps_per_second': 7.564 } ``` | 7cbcb8ea1fbf0b4c27235f53e877a841 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-combined-DS This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0232 - Accuracy: 0.6362 - Precision: 0.6193 - Recall: 0.6204 - F1: 0.6160 | be61386b191aa4d99846036b650d54c5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.1187640010910775e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | b7b51e128c7aa6b175de8b8f31edfe98 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0408 | 1.0 | 711 | 1.0206 | 0.5723 | 0.5597 | 0.5122 | 0.4897 | | 0.9224 | 2.0 |... | 895098e04823813c06c305fba1445b92 |
apache-2.0 | ['generated_from_keras_callback'] | false | Yujun1of1/concrete-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2256 - Validation Loss: 2.6946 - Epoch: 0 | 6cadd4939f5f51f5c482055fc255ec2e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base960-english-phoneme_v3 This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the TIMIT dataset. It achieves the following results on the evaluation set: - Loss: 0.3697 - Cer: 0.0987 | 84d198198bb32c67e7a809a6dee738ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Per | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.2678 | 6.94 | 500 | 0.2347 | 0.0874 | | 0.25 | 13.88 | 1000 | 0.3358 | 0.1122 | | 0.2126 | 20.83 | 1500 | 0.3865 | 0.1131 | |... | 50882f957e823c4ecaef59d5be5ce9b9 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_3e-05_all_16_02_2022-16_29_13 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: 0.1630 - Precision: 0.3684... | f30237b61cdf48270b29ec9abdd0c7a3 |
afl-3.0 | [] | false | Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | rst-all-11b ... | 7c4e773290f612b5d07b979ef7cb2415 |
apache-2.0 | ['generated_from_keras_callback'] | false | silviacamplani/distilbert-finetuned-tapt-ner-music This model is a fine-tuned version of [silviacamplani/distilbert-finetuned-tapt-lm-ai](https://huggingface.co/silviacamplani/distilbert-finetuned-tapt-lm-ai) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6932 - Valida... | 630c1dd9519e8e02c0a58425df0573a4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.7047 | 2.0137 | 0.0 | 0.0 | 0.0 | 0.5482 | 0 ... | 1d05357a2323588f3d78f9131d78e5df |
mit | ['generated_from_trainer'] | false | gallant_beaver This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekko... | ac4f9fbfb34a99dab980965d7c71cefa |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | b02e078427d3624482915aee454c0063 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-squad This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3348 | ffa5b448bfd4b44d06b7dc5b591f03a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.303 | 1.0 | 1997 | 1.2828 | | 0.8647 | 2.0 | 3994 | 1.2168 | | 0.6267 | 3.0 | 5991 | 1.3348 | | 294f997ba223e900ce835ec213a11188 |
creativeml-openrail-m | [] | false | Pinata dreambooth model for Stable-Diffusion Trained on 30 creatures, 2000 steps. With TheLastBen fast-stable-diffusion (https://github.com/TheLastBen/fast-stable-diffusion) use the token **dbvvpinata**  ... | edb235e8fb1d329ac01463c776c6b80a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.742 | 1.0 | 2334 | 3.6593 | | 3.6297 | 2.0 | 4668 | 3.6440 | | 3.5795 | 3.0 | 7002 | 3.6391 | | 702608fc481a82dd5765fbe7e79c89f7 |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-finetuned-FER2013-0.001 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9002 - Accuracy: 0.6847 | f5ea2a12d596672c6c25a0985ea8f756 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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... | ba7d26f8722a3387a0e55fade8257679 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4723 | 1.0 | 224 | 1.3382 | 0.4887 | | 1.2236 | 2.0 | 448 | 1.1090 | 0.5751 | | 1.1728 | 3.0 | 672 | 1.0262 | 0.... | 5c13ec8af4ea637b1e99de28d61e3b5f |
mit | [] | false | alberto mielgo on Stable Diffusion This is the `<street>` 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... | bfe17a1fc80a36578fe558b27ea2a1ae |
apache-2.0 | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9229 - Accuracy: 0.7586 | 2e70c612ccef63667facd2f72ae8513c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9119 | 1.0 | 258 | 0.8750 | 0.7241 | | 0.8307 | 2.0 | 516 | 0.9229 | 0.7586 | | 4cee69b456b3eac08ea243335c2df548 |
apache-2.0 | ['generated_from_trainer'] | false | classification-poems This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the spanish Poems Dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.8228 - Accuracy: 0.7241 | 871c91fed0c292bff41db9aef9b9fd6c |
apache-2.0 | ['generated_from_trainer'] | false | Training and evaluation data The original dataset has the columns author, content, title, year and type of poem. For each example, the type of poem it belongs to is identified. Then the model will recognize which type of poem the entered content belongs to. | 2764d9c028f7f21a28e18174c3b5dda1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9344 | 1.0 | 258 | 0.7505 | 0.7586 | | 0.9239 | 2.0 | 516 | 0.8228 | 0.7241 | | b9b34e32d1c364279581da078905661f |
apache-2.0 | ['transformers', 'text-classification'] | false | Unam_tesis_beto_finnetuning: Unam's thesis classification with BETO This model is created from the finetuning of the pre-model for Spanish [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased), using PyTorch framework, and trained with a set of theses of the National Autonomous University of Mexico... | c45546643b43fd3ec380c465d8492747 |
apache-2.0 | ['transformers', 'text-classification'] | false | Training Dataset 1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career) | Careers | Size | |--------------|----------------------| | Actuaría | 200 | | Derecho| 200 | | Economía| 200 | | Psicología| 200 | | Química Farmac... | 34e3780995cbb86ea6f7f934debd2bfa |
apache-2.0 | ['transformers', 'text-classification'] | false | Example of use For further details on how to use unam_tesis_BETO_finnetuning you can visit the Hugging Face Transformers library, starting with the Quickstart section. The UNAM tesis model can be accessed simply as 'hackathon-pln-e/unam_tesis_BETO_finnetuning' by using the Transformers library. An example of how to d... | 5fb81b4487fce397cc94b5c552bba353 |
apache-2.0 | ['transformers', 'text-classification'] | false | Citation To cite this resource in a publication please use the following: [UNAM's Tesis with BETO finetuning classify] (https://huggingface.co/hackathon-pln-es/unam_tesis_BETO_finnetuning) To cite this resource in a publication please use the following: ``` @inproceedings{SpanishNLPHackaton2022, title={UNAM's Th... | acb40ff06fdefac36ba3fdac7755a4db |
apache-2.0 | ['transformers', 'text-classification'] | false | Team members - Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah)) - Dionis López Ramos ([inoid](https://huggingface.co/inoid)) - Yisel Clavel Quintero ([clavel](https://huggingface.co/clavel)) - Ximena Yeraldin López López ([Ximyer](https://huggingface.co/Ximyer)) | 856766390a0337726eec5470e6f04dd5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 35d2c1d5b91725f85824fdb30e01a101 |
mit | ['generated_from_trainer'] | false | eng_xlmr This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9686 | 9281b96f0364bde7bd2956aeb654dee1 |
mit | [] | false | Usage ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig import torch config = AutoConfig.from_pretrained("bhavitvyamalik/fake-news_xtremedistil-l6-h256-uncased") model = AutoModelForSequenceClassification.from_pretrained("bhavitvyamalik/fake-news_xtremedistil-l6-h256-un... | 56bdb68c43e8b7f3f87b91412c4ff0ff |
apache-2.0 | ['vision', 'image-classification'] | false | Vision Transformer (base-sized model) - Hybrid The hybrid Vision Transformer (ViT) model was proposed in [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unter... | ef7cb15a3a7d97218cb852bfba7bdda8 |
apache-2.0 | ['vision', 'image-classification'] | false | Model description *While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional ne... | fa6d7aa5d654cd58597496a37bd296b5 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import ViTHybridImageProcessor, ViTHybridForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.j... | 4d679daf863d56b6f4940f00640de316 |
apache-2.0 | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) >>> tabby, tabby cat ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html | f1221861d980534f4a9acba14d55bf31 |
apache-2.0 | ['vision', 'image-classification'] | false | Training data The ViT-Hybrid model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. | 39ab811a4d74f63572e62bc3c63358cd |
mit | [] | false | muxoyara on Stable Diffusion This is the `<muxoyara>` 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 tra... | 0955300392d37391dfb76264339e9db2 |
mit | ['generated_from_trainer'] | false | language-detection-RoBert-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1398 - Accuracy: 0.9865 | eaeb324f710ae0cf3cfa3dc95ac9436d |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Pap... | 6c5192ee27894b780d3ea3b4fd6caa4b |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft") model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft") | a6c4aa4189b576f66b4fb28a83bc5f1c |
apache-2.0 | ['generated_from_keras_callback'] | false | merve/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2037 - Validation Loss: 0.0703 - Epoch: 0 | 402c91d1c89b333aa72b9b75ab89951f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-kor-11385 This model is a fine-tuned version of [teddy322/wav2vec2-large-xls-r-300m-kor-11385](https://huggingface.co/teddy322/wav2vec2-large-xls-r-300m-kor-11385) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.4033 - Wer: 0.2805 | c8aedec6af42e51a533ef5ae68f25bb7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | d3c62c4dd962954fe18f78e0732574e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0502 | 1.97 | 400 | 0.4049 | 0.3283 | | 0.0631 | 3.94 | 800 | 0.4618 | 0.3260 | | 0.0508 | 5.91 | 1200 | 0.4391 | 0.3170 | |... | 30ca1bbce21554a1fc6c90389817b499 |
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