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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/DreadPoor/Chimera-7B-TIES
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit", "DreadPoor/Siren-7B-slerp", "S-miguel/The-Trinity-Coder-7B"], "base_model": "DreadPoor/Chimera-7B-TIES", "quantized_by": "mradermacher"} | mradermacher/Chimera-7B-TIES-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"DreadPoor/Siren-7B-slerp",
"S-miguel/The-Trinity-Coder-7B",
"en",
"base_model:DreadPoor/Chimera-7B-TIES",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:05:14+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #merge #mergekit #lazymergekit #DreadPoor/Siren-7B-slerp #S-miguel/The-Trinity-Coder-7B #en #base_model-DreadPoor/Chimera-7B-TIES #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #merge #mergekit #lazymergekit #DreadPoor/Siren-7B-slerp #S-miguel/The-Trinity-Coder-7B #en #base_model-DreadPoor/Chimera-7B-TIES #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/PotatoOff/Michel-13B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "agpl-3.0", "library_name": "transformers", "base_model": "PotatoOff/Michel-13B", "quantized_by": "mradermacher"} | mradermacher/Michel-13B-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:PotatoOff/Michel-13B",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:06:16+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-PotatoOff/Michel-13B #license-agpl-3.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-PotatoOff/Michel-13B #license-agpl-3.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine_tuned_bert
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1299
- F1: 0.8444
- F5: 0.8373
- Precision: 0.8636
- Recall: 0.8261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F5 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
| No log | 1.0 | 33 | 0.3776 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 66 | 0.2996 | 0.4 | 0.3359 | 0.8 | 0.2667 |
| No log | 3.0 | 99 | 0.2137 | 0.7273 | 0.7534 | 0.6667 | 0.8 |
| No log | 4.0 | 132 | 0.2161 | 0.6429 | 0.6258 | 0.6923 | 0.6 |
| No log | 5.0 | 165 | 0.2367 | 0.6154 | 0.5812 | 0.7273 | 0.5333 |
| No log | 6.0 | 198 | 0.1997 | 0.7451 | 0.6980 | 0.9048 | 0.6333 |
| No log | 7.0 | 231 | 0.2023 | 0.8000 | 0.8 | 0.8 | 0.8 |
| No log | 8.0 | 264 | 0.2011 | 0.8070 | 0.7911 | 0.8519 | 0.7667 |
| No log | 9.0 | 297 | 0.2196 | 0.7857 | 0.7648 | 0.8462 | 0.7333 |
| No log | 10.0 | 330 | 0.2509 | 0.7667 | 0.7667 | 0.7667 | 0.7667 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0a0+ebedce2
- Datasets 2.17.1
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "base_model": "google-bert/bert-base-multilingual-cased", "model-index": [{"name": "fine_tuned_bert", "results": []}]} | morten-j/fine_tuned_bert | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:07:32+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| fine\_tuned\_bert
=================
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1299
* F1: 0.8444
* F5: 0.8373
* Precision: 0.8636
* Recall: 0.8261
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.3.0a0+ebedce2
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.3.0a0+ebedce2\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.3.0a0+ebedce2\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-xsum_v2_trained_on_10 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:07:37+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Results",
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] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# selfbiorag-7b-wo-kqa_silver_wogold-sft
This model is a fine-tuned version of [dmis-lab/selfbiorag_7b](https://huggingface.co/dmis-lab/selfbiorag_7b) on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5113 | 0.87 | 5 | 1.3347 |
| 1.2262 | 1.91 | 11 | 1.1637 |
| 1.1423 | 2.61 | 15 | 1.1482 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/deita-10k-v0-sft"], "base_model": "dmis-lab/selfbiorag_7b", "model-index": [{"name": "selfbiorag-7b-wo-kqa_silver_wogold-sft", "results": []}]} | Minbyul/selfbiorag-7b-wo-kqa_silver_wogold-sft | null | [
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"base_model:dmis-lab/selfbiorag_7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:07:54+00:00 | [] | [] | TAGS
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| selfbiorag-7b-wo-kqa\_silver\_wogold-sft
========================================
This model is a fine-tuned version of dmis-lab/selfbiorag\_7b on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1482
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* Tokenizers 0.15.2
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] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-Instruct-sft-en-de
A full finetune of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using a mix of English and German instruction data.
## Dataset
|source|#examples|
|---|---|
teknium/OpenHermes-2.5 |1001551
maxidl/OpenOrca-gpt4-de |119559
maxidl/MathInstruct-de |56793
maxidl/Capybara-de |15991
maxidl/math-prm-800k-de |12298
maxidl/wikihow-de |10103
maxidl/no_robots-de |9500
maxidl/lima-de |1030
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- total_train_batch_size: 128
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "generated_from_trainer"], "datasets": ["maxidl/instruct-en-de"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "Mistral-7B-v0.1-Instruct-sft-en-de", "results": []}]} | maxidl/Mistral-7B-v0.1-Instruct-sft-en-de | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:07:56+00:00 | [] | [] | TAGS
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| Mistral-7B-v0.1-Instruct-sft-en-de
==================================
A full finetune of mistralai/Mistral-7B-v0.1 using a mix of English and German instruction data.
Dataset
-------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 32
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
| {"library_name": "transformers", "tags": ["llama-factory"]} | qsy71/medical_llama2_7b | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:08:05+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #llama-factory #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"## Model Card Contact"
] |
null | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-xsum_v2_trained_on_25 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:08:33+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Environmental Impact
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- Hardware Type:
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### Compute Infrastructure
#### Hardware
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[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Authors [optional]
## Model Card Contact
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null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-xsum_v2_trained_on_50 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:10:38+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-CR-Cluster1di8-ConPre-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-CR-Cluster1di8-ConPre-3Epochs", "results": []}]} | tedad09/PolizzeDonut-CR-Cluster1di8-ConPre-3Epochs | null | [
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"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:11:34+00:00 | [] | [] | TAGS
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|
# PolizzeDonut-CR-Cluster1di8-ConPre-3Epochs
This model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "ybelkada/falcon-7b-sharded-bf16", "model-index": [{"name": "results", "results": []}]} | NeroAce/results | null | [
"peft",
"tensorboard",
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"trl",
"sft",
"generated_from_trainer",
"custom_code",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | null | 2024-04-16T07:11:43+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #falcon #trl #sft #generated_from_trainer #custom_code #base_model-ybelkada/falcon-7b-sharded-bf16 #region-us
|
# results
This model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
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] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dpo_helpfulhelpful_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_helpfulhelpful_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05", "results": []}]} | Holarissun/dpo_helpfulhelpful_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T07:11:54+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
|
# dpo_helpfulhelpful_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# dpo_helpfulhelpful_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 15\n- training_steps: 5000",
"### Training results",
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"# dpo_helpfulhelpful_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 15\n- training_steps: 5000",
"### Training results",
"### Framework versions\n\n- PEFT 0.9.0\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-xsum_v2_trained_on_100 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:13:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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"## Model Details",
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"## Model Card Contact"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esm2_t30_150M_UR50D-finetuned-secondary-structure
This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0924
- Accuracy: 0.9829
- Mcc: 0.3696
- Recall: 0.3004
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Mcc | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| No log | 1.0 | 128 | 0.0845 | 0.9844 | 0.2665 | 0.1033 |
| No log | 2.0 | 256 | 0.0815 | 0.9842 | 0.2316 | 0.0782 |
| No log | 3.0 | 384 | 0.0793 | 0.9852 | 0.3837 | 0.2325 |
| 0.0761 | 4.0 | 512 | 0.0842 | 0.9841 | 0.3854 | 0.2880 |
| 0.0761 | 5.0 | 640 | 0.0924 | 0.9829 | 0.3696 | 0.3004 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "recall"], "base_model": "facebook/esm2_t30_150M_UR50D", "model-index": [{"name": "esm2_t30_150M_UR50D-finetuned-secondary-structure", "results": []}]} | theViolet/esm2_t30_150M_UR50D-finetuned-secondary-structure | null | [
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"base_model:facebook/esm2_t30_150M_UR50D",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:14:20+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #esm #token-classification #generated_from_trainer #base_model-facebook/esm2_t30_150M_UR50D #license-mit #autotrain_compatible #endpoints_compatible #region-us
| esm2\_t30\_150M\_UR50D-finetuned-secondary-structure
====================================================
This model is a fine-tuned version of facebook/esm2\_t30\_150M\_UR50D on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0924
* Accuracy: 0.9829
* Mcc: 0.3696
* Recall: 0.3004
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 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: 5
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-rocstories
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:
- Loss: 0.4850
- Accuracy: 0.9023
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 299 | 0.5701 | 0.6861 |
| 0.5927 | 2.0 | 598 | 0.3495 | 0.8571 |
| 0.5927 | 3.0 | 897 | 0.3490 | 0.8891 |
| 0.2228 | 4.0 | 1196 | 0.4850 | 0.9023 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-rocstories", "results": []}]} | KeiHeityuu/bert-base-cased-rocstories | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:14:49+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-rocstories
==========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4850
* Accuracy: 0.9023
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# poly-a2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 70.0093 | 0.35 | 10 | nan | 1.0 |
| 0.0 | 0.7 | 20 | nan | 1.0 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["minds14"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "poly-a2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "minds14", "type": "minds14", "config": "en-US", "split": "train[:2000]", "args": "en-US"}, "metrics": [{"type": "wer", "value": 1.0, "name": "Wer"}]}]}]} | Hemg/poly-a2 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:15:00+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us
| poly-a2
=======
This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5
* training\_steps: 20
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2+cpu
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2+cpu\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2+cpu\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | ---
## How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("linh5nb/Llama-2-7b-chat-finetune_covid")
tokenizer = AutoTokenizer.from_pretrained("linh5nb/Llama-2-7b-chat-finetune_covid")
user_input = '''When was the West African Ebolavirus outbreak?'''
our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this
your_system_prompt = "Please, answer this question faithfully."
prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
### Training Data
https://huggingface.co/datasets/hodgesz/covid_qa_llama2
| {} | linh5nb/Llama-2-7b-chat-finetune_covid | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:16:18+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
## How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("linh5nb/Llama-2-7b-chat-finetune_covid")
tokenizer = AutoTokenizer.from_pretrained("linh5nb/Llama-2-7b-chat-finetune_covid")
user_input = '''When was the West African Ebolavirus outbreak?'''
our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this
your_system_prompt = "Please, answer this question faithfully."
prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(URL)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(URL[-1])
pred = URL(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
### Training Data
URL
| [
"## How to Get Started with the Model\n\nUse the code below to get started with the model.\n\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel = AutoModelForCausalLM.from_pretrained(\"linh5nb/Llama-2-7b-chat-finetune_covid\")\n\ntokenizer = AutoTokenizer.from_pretrained(\"linh5nb/Llama-2-7b-chat-finetune_covid\")\n\nuser_input = '''When was the West African Ebolavirus outbreak?'''\n\n\nour_system_prompt = \"\\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\\n\" # Please do NOT change this\n\nyour_system_prompt = \"Please, answer this question faithfully.\"\n\nprompt = f\"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\\n\\n{your_system_prompt}\\n{user_input} [/INST]\"\n\ninputs = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=False).input_ids.to(URL)\n\noutputs = model.generate(input_ids=inputs, max_length=4096)[0]\n\nanswer_start = int(URL[-1])\n\npred = URL(outputs[answer_start:], skip_special_tokens=True)\n\nprint(f'### User Input:\\n{user_input}\\n\\n### Assistant Output:\\n{pred}')",
"### Training Data\n\nURL"
] | [
"TAGS\n#transformers #pytorch #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.\n\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel = AutoModelForCausalLM.from_pretrained(\"linh5nb/Llama-2-7b-chat-finetune_covid\")\n\ntokenizer = AutoTokenizer.from_pretrained(\"linh5nb/Llama-2-7b-chat-finetune_covid\")\n\nuser_input = '''When was the West African Ebolavirus outbreak?'''\n\n\nour_system_prompt = \"\\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\\n\" # Please do NOT change this\n\nyour_system_prompt = \"Please, answer this question faithfully.\"\n\nprompt = f\"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\\n\\n{your_system_prompt}\\n{user_input} [/INST]\"\n\ninputs = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=False).input_ids.to(URL)\n\noutputs = model.generate(input_ids=inputs, max_length=4096)[0]\n\nanswer_start = int(URL[-1])\n\npred = URL(outputs[answer_start:], skip_special_tokens=True)\n\nprint(f'### User Input:\\n{user_input}\\n\\n### Assistant Output:\\n{pred}')",
"### Training Data\n\nURL"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/zephyr-7B-alpha-GPTQ", "model-index": [{"name": "zephyr-support-chatbot", "results": []}]} | Rasi1610/zephyr-support-chatbot | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
] | null | 2024-04-16T07:16:23+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-alpha-GPTQ #license-mit #region-us
|
# zephyr-support-chatbot
This model is a fine-tuned version of TheBloke/zephyr-7B-alpha-GPTQ on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# zephyr-support-chatbot\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-alpha-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-alpha-GPTQ #license-mit #region-us \n",
"# zephyr-support-chatbot\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-alpha-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Majidni/openchat_model_AIM | null | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:16:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #feature-extraction #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #feature-extraction #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-CR-Cluster1di7-ConPre-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-CR-Cluster1di7-ConPre-3Epochs", "results": []}]} | tedad09/PolizzeDonut-CR-Cluster1di7-ConPre-3Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:17:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 #license-mit #endpoints_compatible #region-us
|
# PolizzeDonut-CR-Cluster1di7-ConPre-3Epochs
This model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# PolizzeDonut-CR-Cluster1di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
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"# PolizzeDonut-CR-Cluster1di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K14ac-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- F1 Score: 0.6114
- Accuracy: 0.6124
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6689 | 15.38 | 200 | 0.6480 | 0.6028 | 0.6151 |
| 0.6227 | 30.77 | 400 | 0.6630 | 0.6188 | 0.6254 |
| 0.6016 | 46.15 | 600 | 0.6742 | 0.6182 | 0.6269 |
| 0.5831 | 61.54 | 800 | 0.6870 | 0.6012 | 0.5991 |
| 0.5685 | 76.92 | 1000 | 0.6940 | 0.6034 | 0.6024 |
| 0.5571 | 92.31 | 1200 | 0.6879 | 0.6084 | 0.6148 |
| 0.5481 | 107.69 | 1400 | 0.7119 | 0.6124 | 0.6118 |
| 0.5411 | 123.08 | 1600 | 0.6881 | 0.6114 | 0.6139 |
| 0.5355 | 138.46 | 1800 | 0.7043 | 0.6182 | 0.6163 |
| 0.5283 | 153.85 | 2000 | 0.7009 | 0.6101 | 0.6121 |
| 0.5227 | 169.23 | 2200 | 0.7124 | 0.6135 | 0.6124 |
| 0.5162 | 184.62 | 2400 | 0.7225 | 0.6160 | 0.6139 |
| 0.5096 | 200.0 | 2600 | 0.7095 | 0.6176 | 0.6209 |
| 0.5017 | 215.38 | 2800 | 0.7118 | 0.6129 | 0.6175 |
| 0.4948 | 230.77 | 3000 | 0.7161 | 0.6103 | 0.6142 |
| 0.49 | 246.15 | 3200 | 0.7210 | 0.6107 | 0.6133 |
| 0.4818 | 261.54 | 3400 | 0.7512 | 0.6127 | 0.6115 |
| 0.4764 | 276.92 | 3600 | 0.7430 | 0.6147 | 0.6151 |
| 0.4688 | 292.31 | 3800 | 0.7443 | 0.6125 | 0.6157 |
| 0.463 | 307.69 | 4000 | 0.7533 | 0.6192 | 0.6197 |
| 0.4569 | 323.08 | 4200 | 0.7514 | 0.6170 | 0.6188 |
| 0.4506 | 338.46 | 4400 | 0.7730 | 0.6118 | 0.6115 |
| 0.4461 | 353.85 | 4600 | 0.7576 | 0.6143 | 0.6182 |
| 0.4391 | 369.23 | 4800 | 0.7808 | 0.6111 | 0.6109 |
| 0.4354 | 384.62 | 5000 | 0.7910 | 0.6071 | 0.6057 |
| 0.4281 | 400.0 | 5200 | 0.7869 | 0.6102 | 0.6133 |
| 0.4233 | 415.38 | 5400 | 0.7910 | 0.6041 | 0.6018 |
| 0.4168 | 430.77 | 5600 | 0.7945 | 0.6104 | 0.6130 |
| 0.4135 | 446.15 | 5800 | 0.8160 | 0.6096 | 0.6109 |
| 0.4077 | 461.54 | 6000 | 0.8025 | 0.6142 | 0.6151 |
| 0.4054 | 476.92 | 6200 | 0.8280 | 0.6117 | 0.6115 |
| 0.3994 | 492.31 | 6400 | 0.7993 | 0.6105 | 0.6112 |
| 0.3953 | 507.69 | 6600 | 0.8102 | 0.6159 | 0.6160 |
| 0.3916 | 523.08 | 6800 | 0.8184 | 0.6111 | 0.6118 |
| 0.3896 | 538.46 | 7000 | 0.8210 | 0.6117 | 0.6115 |
| 0.3853 | 553.85 | 7200 | 0.8207 | 0.6131 | 0.6139 |
| 0.3829 | 569.23 | 7400 | 0.8341 | 0.6102 | 0.6091 |
| 0.3794 | 584.62 | 7600 | 0.8297 | 0.6121 | 0.6121 |
| 0.3756 | 600.0 | 7800 | 0.8333 | 0.6085 | 0.6091 |
| 0.3736 | 615.38 | 8000 | 0.8338 | 0.6130 | 0.6121 |
| 0.3719 | 630.77 | 8200 | 0.8363 | 0.6115 | 0.6106 |
| 0.37 | 646.15 | 8400 | 0.8347 | 0.6166 | 0.6160 |
| 0.368 | 661.54 | 8600 | 0.8306 | 0.6121 | 0.6118 |
| 0.3667 | 676.92 | 8800 | 0.8423 | 0.6119 | 0.6121 |
| 0.3646 | 692.31 | 9000 | 0.8398 | 0.6126 | 0.6130 |
| 0.3622 | 707.69 | 9200 | 0.8432 | 0.6131 | 0.6130 |
| 0.3617 | 723.08 | 9400 | 0.8413 | 0.6124 | 0.6121 |
| 0.3604 | 738.46 | 9600 | 0.8460 | 0.6121 | 0.6115 |
| 0.3594 | 753.85 | 9800 | 0.8441 | 0.6127 | 0.6127 |
| 0.3603 | 769.23 | 10000 | 0.8442 | 0.6127 | 0.6124 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T07:17:53+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_EMP\_H3K14ac-seqsight\_16384\_512\_22M-L32\_all
====================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_EMP\_H3K14ac dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7720
* F1 Score: 0.6114
* Accuracy: 0.6124
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
null | transformers |
# Model Card for Starcoder2-3B Fine-Tuned with QLORA on Solidity Dataset
## Model Details
### Model Description
This model has been fine-tuned using QLORA specifically for generating Solidity smart contracts from natural language instructions.
- **Developed by:** @yoniebans
- **Model type:** Transformer-based Causal Language Model
- **Language(s) (NLP):** English with programming language syntax (Solidity)
- **License:** [More Information Needed]
- **Finetuned from model** bigcode/starcoder2-3b
### Model Sources [optional]
- **Repository:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
### Direct Use
This model is designed to demonstrate how fine-tuning a base model such as starcoder2-3b using qlora with a targetted dataset can increase the model's ability to create Solidity code directly from natural language descriptions or instructions.
### Out-of-Scope Use
This model is not intended for:
- Deployment in production systems without rigorous testing.
- Use in non-technical text generation or any context outside smart contract development.
## Bias, Risks, and Limitations
The training data primarily consists of code from publicly sourced Solidity projects which may not encompass the full diversity of programming styles and techniques.
### Recommendations
Users are advised to use this model as a starting point for development and not as a definitive solution. Generated code should always be reviewed by experienced developers to ensure security and functionality.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import sys
from peft import PeftModel
from transformers import BitsAndBytesConfig, AutoTokenizer, AutoModelForCausalLM
import torch
import accelerate
use_4bit = True
bnb_4bit_compute_dtype = "float32"
bnb_4bit_quant_type = "nf4"
use_double_nested_quant = True
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
device_map = "auto"
max_memory = '45000MB'
n_gpus = torch.cuda.device_count()
max_memory = {i: max_memory for i in range(n_gpus)}
model = AutoModelForCausalLM.from_pretrained(
"bigcode/starcoder2-3b",
cache_dir=None,
device_map=device_map,
max_memory=max_memory,
quantization_config=BitsAndBytesConfig(
load_in_4bit=use_4bit,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_double_nested_quant,
bnb_4bit_quant_type=bnb_4bit_quant_type
),
torch_dtype=torch.float32,
trust_remote_code=False
)
adapter_weights = "yoniebans/starcoder2-3b-qlora-solidity"
model = PeftModel.from_pretrained(model, adapter_weights)
tokenizer = AutoTokenizer.from_pretrained(
checkpoint,
cache_dir=None,
padding_side="right",
use_fast=False,
tokenizer_type=None, # Needed for HF name change
trust_remote_code=False,
use_auth_token=False,
)
input='Make a smart contract for a memecoin named 'LLMAI', adhering to the ERC20 standard. The contract should enforce a purchase limit where no individual wallet can acquire more than 1% of the total token supply, which is set at 10 billion tokens. This purchasing limit should be modifiable and can only be disabled by the contract owner at their discretion. Note that the interfaces for ERC20, Ownable, and any other dependencies should be assumed as already imported and do not need to be included in your code response.'
prompt = f"""### Instruction:
Use the Task below and the Input given to write the Response, which is a programming code that can solve the following Task:
### Task:
{input}
### Solution:
"""
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(
input_ids=input_ids,
max_new_tokens=2048,
do_sample=True,
top_p=0.9,
temperature=0.001,
pad_token_id=1
)
output_text = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
output_text_without_prompt = output_text[len(prompt):]
file_path = './smart_contract.sol'
with open(file_path, 'w') as file:
file.write(output_text_without_prompt)
print(f"Output written to {file_path}")
```
## Training Details
### Training Data
The model was trained on a dataset consisting of pairs of natural language instructions and their corresponding Solidity code implementations. This dataset includes over 6000 input and outputs.
### Training Procedure
#### Training Hyperparameters
- Number of train epochs: 30
- Double quant: true
- Quant type: nf4
- Bits: 4
- Lora r: 64
- Lora aplha: 16
- Lora dropout: 0.0
- Per device train batch size: 1
- Gradient accumulation steps: 1
- Max steps: 10000
- Weight decay: 0.0 #use lora dropout instead for regularization if needed
- Learning rate: 2e-4
- Max gradient normal: 0.3
- Gradient checkpointing: true
- FP16: false
- FP16 option level: O1
- BF16: false
- Optimizer: paged AdamW 32-bit
- Learning rate scheduler type: constant
- Warmup ratio: 0.03
#### Speeds, Sizes, Times [optional]
| Epoch | Grad Norm | Loss | Step |
|-------|-----------|------|------|
| 0.03 | 0.513248 | 2.0948 | 10 |
| 0.59 | 0.976398 | 0.7952 | 200 |
| 1.18 | 0.291436 | 0.5081 | 400 |
| 1.78 | 0.197318 | 0.5007 | 600 |
| 2.37 | 0.143200 | 0.4189 | 800 |
| 2.96 | 0.153539 | 0.4314 | 1000 |
| 3.55 | 0.199708 | 0.4554 | 1200 |
| 4.15 | 0.258209 | 0.4856 | 1400 |
| 4.74 | 0.295185 | 0.5446 | 1600 |
| 5.33 | 0.157903 | 0.3209 | 1800 |
| 5.92 | 0.149965 | 0.3203 | 2000 |
| 6.52 | 0.166200 | 0.4046 | 2200 |
| 7.11 | 0.181177 | 0.2157 | 2400 |
| 7.70 | 0.156556 | 0.2937 | 2600 |
| 8.29 | 0.309143 | 0.2928 | 2800 |
| 8.89 | 0.211304 | 0.3414 | 3000 |
| 9.48 | 0.166119 | 0.2716 | 3200 |
| 10.07 | 0.265576 | 0.1727 | 3400 |
| 10.66 | 0.231398 | 0.2219 | 3600 |
| 11.26 | 0.183477 | 0.1706 | 3800 |
| 11.85 | 0.182451 | 0.1471 | 4000 |
| 12.44 | 0.272920 | 0.1793 | 4200 |
| 13.03 | 0.189667 | 0.1241 | 4400 |
| 13.62 | 0.136364 | 0.1278 | 4600 |
| 14.22 | 0.297066 | 0.1043 | 4800 |
| 14.81 | 0.213272 | 0.1760 | 5000 |
| 15.40 | 0.162718 | 0.1062 | 5200 |
| 15.99 | 0.229559 | 0.1012 | 5400 |
| 16.59 | 0.271037 | 0.1180 | 5600 |
| 17.18 | 0.239012 | 0.0871 | 5800 |
| 17.77 | 0.175727 | 0.0894 | 6000 |
| 18.36 | 0.151963 | 0.1154 | 6200 |
| 18.96 | 0.202392 | 0.1096 | 6400 |
| 19.55 | 0.202703 | 0.0764 | 6600 |
| 20.18 | 0.148534 | 0.0551 | 6800 |
| 20.77 | 0.151745 | 0.0599 | 7000 |
| 21.36 | 0.188332 | 0.0707 | 7200 |
| 21.95 | 0.190111 | 0.0987 | 7400 |
| 22.55 | 0.163640 | 0.0567 | 7600 |
| 23.14 | 0.209854 | 0.0534 | 7800 |
| 23.73 | 0.225155 | 0.0626 | 8000 |
| 24.32 | 0.172093 | 0.0389 | 8200 |
| 24.92 | 0.162895 | 0.0347 | 8400 |
| 25.52 | 0.187249 | 0.0592 | 8600 |
| 26.11 | 0.182813 | 0.0368 | 8800 |
| 26.70 | 0.243531 | 0.0427 | 9000 |
| 27.29 | 0.190624 | 0.0325 | 9200 |
| 27.89 | 0.189085 | 0.0311 | 9400 |
| 28.48 | 0.171129 | 0.0322 | 9600 |
| 29.07 | 0.138704 | 0.0353 | 9800 |
| 29.66 | 0.216177 | 0.0312 | 10000 |
### Results
#### Summary
Initial evaluations show promising results in generating Solidity code, with further testing planned.
## Environmental Impact
- **Hardware Type:** NVIDIA A40 GPU, 48GB VRAM, 48GB RAM, 9vCPU
- **Hours used:** 18
- **Cloud Provider:** runpod.io
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Citation [optional]
**BibTeX:**
```
@misc{lozhkov2024starcoder,
title={StarCoder 2 and The Stack v2: The Next Generation},
author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
year={2024},
eprint={2402.19173},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
```
**APA:**
Starcoder2-3B: BigCode Team. (2024). Starcoder2-3B [Software]. Available from https://huggingface.co/bigcode/starcoder2-3b
AlfredPros Smart Contracts Instructions: AlfredPros. (2023). Smart Contracts Instructions [Data set]. Available from https://huggingface.co/datasets/AlfredPros/smart-contracts-instructions
## Model Card Contact
[email protected] | {"language": ["en"], "license": "bigcode-openrail-m", "library_name": "transformers", "datasets": ["AlfredPros/smart-contracts-instructions"]} | yoniebans/starcoder2-3b-qlora-solidity | null | [
"transformers",
"safetensors",
"en",
"dataset:AlfredPros/smart-contracts-instructions",
"arxiv:2402.19173",
"license:bigcode-openrail-m",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:19:15+00:00 | [
"2402.19173"
] | [
"en"
] | TAGS
#transformers #safetensors #en #dataset-AlfredPros/smart-contracts-instructions #arxiv-2402.19173 #license-bigcode-openrail-m #endpoints_compatible #region-us
| Model Card for Starcoder2-3B Fine-Tuned with QLORA on Solidity Dataset
======================================================================
Model Details
-------------
### Model Description
This model has been fine-tuned using QLORA specifically for generating Solidity smart contracts from natural language instructions.
* Developed by: @yoniebans
* Model type: Transformer-based Causal Language Model
* Language(s) (NLP): English with programming language syntax (Solidity)
* License:
* Finetuned from model bigcode/starcoder2-3b
### Model Sources [optional]
* Repository:
* Demo [optional]:
Uses
----
### Direct Use
This model is designed to demonstrate how fine-tuning a base model such as starcoder2-3b using qlora with a targetted dataset can increase the model's ability to create Solidity code directly from natural language descriptions or instructions.
### Out-of-Scope Use
This model is not intended for:
* Deployment in production systems without rigorous testing.
* Use in non-technical text generation or any context outside smart contract development.
Bias, Risks, and Limitations
----------------------------
The training data primarily consists of code from publicly sourced Solidity projects which may not encompass the full diversity of programming styles and techniques.
### Recommendations
Users are advised to use this model as a starting point for development and not as a definitive solution. Generated code should always be reviewed by experienced developers to ensure security and functionality.
How to Get Started with the Model
---------------------------------
Use the code below to get started with the model.
Training Details
----------------
### Training Data
The model was trained on a dataset consisting of pairs of natural language instructions and their corresponding Solidity code implementations. This dataset includes over 6000 input and outputs.
### Training Procedure
#### Training Hyperparameters
* Number of train epochs: 30
* Double quant: true
* Quant type: nf4
* Bits: 4
* Lora r: 64
* Lora aplha: 16
* Lora dropout: 0.0
* Per device train batch size: 1
* Gradient accumulation steps: 1
* Max steps: 10000
* Weight decay: 0.0 #use lora dropout instead for regularization if needed
* Learning rate: 2e-4
* Max gradient normal: 0.3
* Gradient checkpointing: true
* FP16: false
* FP16 option level: O1
* BF16: false
* Optimizer: paged AdamW 32-bit
* Learning rate scheduler type: constant
* Warmup ratio: 0.03
#### Speeds, Sizes, Times [optional]
!image/png
### Results
#### Summary
Initial evaluations show promising results in generating Solidity code, with further testing planned.
Environmental Impact
--------------------
* Hardware Type: NVIDIA A40 GPU, 48GB VRAM, 48GB RAM, 9vCPU
* Hours used: 18
* Cloud Provider: URL
* Compute Region:
* Carbon Emitted:
[optional]
BibTeX:
APA:
Starcoder2-3B: BigCode Team. (2024). Starcoder2-3B [Software]. Available from URL
AlfredPros Smart Contracts Instructions: AlfredPros. (2023). Smart Contracts Instructions [Data set]. Available from URL
Model Card Contact
------------------
jonny@URL
| [
"### Model Description\n\n\nThis model has been fine-tuned using QLORA specifically for generating Solidity smart contracts from natural language instructions.\n\n\n* Developed by: @yoniebans\n* Model type: Transformer-based Causal Language Model\n* Language(s) (NLP): English with programming language syntax (Solidity)\n* License:\n* Finetuned from model bigcode/starcoder2-3b",
"### Model Sources [optional]\n\n\n* Repository:\n* Demo [optional]:\n\n\nUses\n----",
"### Direct Use\n\n\nThis model is designed to demonstrate how fine-tuning a base model such as starcoder2-3b using qlora with a targetted dataset can increase the model's ability to create Solidity code directly from natural language descriptions or instructions.",
"### Out-of-Scope Use\n\n\nThis model is not intended for:\n\n\n* Deployment in production systems without rigorous testing.\n* Use in non-technical text generation or any context outside smart contract development.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nThe training data primarily consists of code from publicly sourced Solidity projects which may not encompass the full diversity of programming styles and techniques.",
"### Recommendations\n\n\nUsers are advised to use this model as a starting point for development and not as a definitive solution. Generated code should always be reviewed by experienced developers to ensure security and functionality.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nTraining Details\n----------------",
"### Training Data\n\n\nThe model was trained on a dataset consisting of pairs of natural language instructions and their corresponding Solidity code implementations. This dataset includes over 6000 input and outputs.",
"### Training Procedure",
"#### Training Hyperparameters\n\n\n* Number of train epochs: 30\n* Double quant: true\n* Quant type: nf4\n* Bits: 4\n* Lora r: 64\n* Lora aplha: 16\n* Lora dropout: 0.0\n* Per device train batch size: 1\n* Gradient accumulation steps: 1\n* Max steps: 10000\n* Weight decay: 0.0 #use lora dropout instead for regularization if needed\n* Learning rate: 2e-4\n* Max gradient normal: 0.3\n* Gradient checkpointing: true\n* FP16: false\n* FP16 option level: O1\n* BF16: false\n* Optimizer: paged AdamW 32-bit\n* Learning rate scheduler type: constant\n* Warmup ratio: 0.03",
"#### Speeds, Sizes, Times [optional]\n\n\n\n!image/png",
"### Results",
"#### Summary\n\n\nInitial evaluations show promising results in generating Solidity code, with further testing planned.\n\n\nEnvironmental Impact\n--------------------\n\n\n* Hardware Type: NVIDIA A40 GPU, 48GB VRAM, 48GB RAM, 9vCPU\n* Hours used: 18\n* Cloud Provider: URL\n* Compute Region:\n* Carbon Emitted:\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nStarcoder2-3B: BigCode Team. (2024). Starcoder2-3B [Software]. Available from URL\n\n\nAlfredPros Smart Contracts Instructions: AlfredPros. (2023). Smart Contracts Instructions [Data set]. Available from URL\n\n\nModel Card Contact\n------------------\n\n\njonny@URL"
] | [
"TAGS\n#transformers #safetensors #en #dataset-AlfredPros/smart-contracts-instructions #arxiv-2402.19173 #license-bigcode-openrail-m #endpoints_compatible #region-us \n",
"### Model Description\n\n\nThis model has been fine-tuned using QLORA specifically for generating Solidity smart contracts from natural language instructions.\n\n\n* Developed by: @yoniebans\n* Model type: Transformer-based Causal Language Model\n* Language(s) (NLP): English with programming language syntax (Solidity)\n* License:\n* Finetuned from model bigcode/starcoder2-3b",
"### Model Sources [optional]\n\n\n* Repository:\n* Demo [optional]:\n\n\nUses\n----",
"### Direct Use\n\n\nThis model is designed to demonstrate how fine-tuning a base model such as starcoder2-3b using qlora with a targetted dataset can increase the model's ability to create Solidity code directly from natural language descriptions or instructions.",
"### Out-of-Scope Use\n\n\nThis model is not intended for:\n\n\n* Deployment in production systems without rigorous testing.\n* Use in non-technical text generation or any context outside smart contract development.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nThe training data primarily consists of code from publicly sourced Solidity projects which may not encompass the full diversity of programming styles and techniques.",
"### Recommendations\n\n\nUsers are advised to use this model as a starting point for development and not as a definitive solution. Generated code should always be reviewed by experienced developers to ensure security and functionality.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nTraining Details\n----------------",
"### Training Data\n\n\nThe model was trained on a dataset consisting of pairs of natural language instructions and their corresponding Solidity code implementations. This dataset includes over 6000 input and outputs.",
"### Training Procedure",
"#### Training Hyperparameters\n\n\n* Number of train epochs: 30\n* Double quant: true\n* Quant type: nf4\n* Bits: 4\n* Lora r: 64\n* Lora aplha: 16\n* Lora dropout: 0.0\n* Per device train batch size: 1\n* Gradient accumulation steps: 1\n* Max steps: 10000\n* Weight decay: 0.0 #use lora dropout instead for regularization if needed\n* Learning rate: 2e-4\n* Max gradient normal: 0.3\n* Gradient checkpointing: true\n* FP16: false\n* FP16 option level: O1\n* BF16: false\n* Optimizer: paged AdamW 32-bit\n* Learning rate scheduler type: constant\n* Warmup ratio: 0.03",
"#### Speeds, Sizes, Times [optional]\n\n\n\n!image/png",
"### Results",
"#### Summary\n\n\nInitial evaluations show promising results in generating Solidity code, with further testing planned.\n\n\nEnvironmental Impact\n--------------------\n\n\n* Hardware Type: NVIDIA A40 GPU, 48GB VRAM, 48GB RAM, 9vCPU\n* Hours used: 18\n* Cloud Provider: URL\n* Compute Region:\n* Carbon Emitted:\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nStarcoder2-3B: BigCode Team. (2024). Starcoder2-3B [Software]. Available from URL\n\n\nAlfredPros Smart Contracts Instructions: AlfredPros. (2023). Smart Contracts Instructions [Data set]. Available from URL\n\n\nModel Card Contact\n------------------\n\n\njonny@URL"
] |
null | transformers |
# Uploaded model
- **Developed by:** NavyaAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/tinyllama-bnb-4bit"} | NavyaAI/FinBot-TinyLLama-LoRA-GGUF-8bit | null | [
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"unsloth",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:20:02+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: NavyaAI
- License: apache-2.0
- Finetuned from model : unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: NavyaAI\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
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"# Uploaded model\n\n- Developed by: NavyaAI\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | AlanYR/QA_tuned_kakao_model | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:20:39+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #deberta-v2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Direct Use",
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"## Training Details",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #deberta-v2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
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"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
fill-mask | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | cloudrambler/selfiesbert | null | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:20:45+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #fill-mask #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"TAGS\n#transformers #safetensors #bert #fill-mask #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Model Card Contact"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me2-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7274
- F1 Score: 0.5879
- Accuracy: 0.5976
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6593 | 16.67 | 200 | 0.6643 | 0.5986 | 0.6025 |
| 0.618 | 33.33 | 400 | 0.6703 | 0.5926 | 0.6031 |
| 0.597 | 50.0 | 600 | 0.6980 | 0.5851 | 0.5852 |
| 0.5772 | 66.67 | 800 | 0.7019 | 0.5869 | 0.5881 |
| 0.5602 | 83.33 | 1000 | 0.7154 | 0.5977 | 0.6002 |
| 0.5491 | 100.0 | 1200 | 0.7172 | 0.6000 | 0.6008 |
| 0.5407 | 116.67 | 1400 | 0.7190 | 0.6007 | 0.6158 |
| 0.535 | 133.33 | 1600 | 0.7156 | 0.6026 | 0.6132 |
| 0.5299 | 150.0 | 1800 | 0.7297 | 0.6006 | 0.6051 |
| 0.5232 | 166.67 | 2000 | 0.7380 | 0.5994 | 0.6012 |
| 0.5185 | 183.33 | 2200 | 0.7322 | 0.5982 | 0.6087 |
| 0.5121 | 200.0 | 2400 | 0.7629 | 0.5954 | 0.5960 |
| 0.5062 | 216.67 | 2600 | 0.7390 | 0.5956 | 0.5973 |
| 0.499 | 233.33 | 2800 | 0.7781 | 0.5999 | 0.6054 |
| 0.492 | 250.0 | 3000 | 0.7682 | 0.5983 | 0.5999 |
| 0.484 | 266.67 | 3200 | 0.8069 | 0.6006 | 0.6054 |
| 0.477 | 283.33 | 3400 | 0.7745 | 0.5846 | 0.5846 |
| 0.4686 | 300.0 | 3600 | 0.8130 | 0.5842 | 0.5859 |
| 0.4596 | 316.67 | 3800 | 0.8381 | 0.5856 | 0.5865 |
| 0.4527 | 333.33 | 4000 | 0.8088 | 0.5822 | 0.5855 |
| 0.445 | 350.0 | 4200 | 0.8371 | 0.5865 | 0.5956 |
| 0.4407 | 366.67 | 4400 | 0.8259 | 0.5778 | 0.5764 |
| 0.4328 | 383.33 | 4600 | 0.8711 | 0.5828 | 0.5833 |
| 0.4281 | 400.0 | 4800 | 0.8483 | 0.5852 | 0.5868 |
| 0.4208 | 416.67 | 5000 | 0.8669 | 0.5858 | 0.5872 |
| 0.4152 | 433.33 | 5200 | 0.8610 | 0.5932 | 0.5969 |
| 0.4092 | 450.0 | 5400 | 0.8707 | 0.5849 | 0.5891 |
| 0.4046 | 466.67 | 5600 | 0.8702 | 0.5849 | 0.5891 |
| 0.3997 | 483.33 | 5800 | 0.8661 | 0.5775 | 0.5800 |
| 0.3929 | 500.0 | 6000 | 0.8846 | 0.5728 | 0.5722 |
| 0.3905 | 516.67 | 6200 | 0.8784 | 0.5809 | 0.5810 |
| 0.3867 | 533.33 | 6400 | 0.8877 | 0.5783 | 0.5803 |
| 0.3815 | 550.0 | 6600 | 0.9361 | 0.5762 | 0.5745 |
| 0.3778 | 566.67 | 6800 | 0.9112 | 0.5773 | 0.5767 |
| 0.3734 | 583.33 | 7000 | 0.9156 | 0.5758 | 0.5758 |
| 0.3698 | 600.0 | 7200 | 0.9173 | 0.5763 | 0.5761 |
| 0.3668 | 616.67 | 7400 | 0.9128 | 0.5731 | 0.5722 |
| 0.3639 | 633.33 | 7600 | 0.9141 | 0.5762 | 0.5780 |
| 0.3618 | 650.0 | 7800 | 0.9274 | 0.5776 | 0.5810 |
| 0.3592 | 666.67 | 8000 | 0.9320 | 0.5795 | 0.5836 |
| 0.3561 | 683.33 | 8200 | 0.9342 | 0.5806 | 0.5826 |
| 0.3546 | 700.0 | 8400 | 0.9470 | 0.5780 | 0.5793 |
| 0.3504 | 716.67 | 8600 | 0.9467 | 0.5795 | 0.5829 |
| 0.3504 | 733.33 | 8800 | 0.9415 | 0.5770 | 0.5787 |
| 0.3471 | 750.0 | 9000 | 0.9646 | 0.5743 | 0.5751 |
| 0.3457 | 766.67 | 9200 | 0.9445 | 0.5753 | 0.5764 |
| 0.3461 | 783.33 | 9400 | 0.9514 | 0.5776 | 0.5793 |
| 0.3441 | 800.0 | 9600 | 0.9564 | 0.5758 | 0.5771 |
| 0.3439 | 816.67 | 9800 | 0.9595 | 0.5781 | 0.5797 |
| 0.3419 | 833.33 | 10000 | 0.9634 | 0.5777 | 0.5790 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T07:21:41+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_EMP\_H3K4me2-seqsight\_16384\_512\_22M-L32\_all
====================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7274
* F1 Score: 0.5879
* Accuracy: 0.5976
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
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] |
null | transformers |
# Uploaded model
- **Developed by:** NavyaAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/tinyllama-bnb-4bit"} | NavyaAI/FinBot-TinyLLama-LoRA-GGUF-16bit | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:22:41+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: NavyaAI
- License: apache-2.0
- Finetuned from model : unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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] |
multiple-choice | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
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: 1.0250
- Accuracy: 0.7360
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 313 | 0.7532 | 0.7112 |
| 0.8021 | 2.0 | 626 | 0.7671 | 0.7216 |
| 0.8021 | 3.0 | 939 | 1.0250 | 0.7360 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | sahithya20/bert-base-uncased-finetuned-swag | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:23:37+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #multiple-choice #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-swag
================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0250
* Accuracy: 0.7360
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Tokenizers 0.15.2
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #multiple-choice #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Tokenizers 0.15.2"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-CR-Cluster2di7-ConPre-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-CR-Cluster2di7-ConPre-3Epochs", "results": []}]} | tedad09/PolizzeDonut-CR-Cluster2di7-ConPre-3Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:23:38+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 #license-mit #endpoints_compatible #region-us
|
# PolizzeDonut-CR-Cluster2di7-ConPre-3Epochs
This model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# PolizzeDonut-CR-Cluster2di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 #license-mit #endpoints_compatible #region-us \n",
"# PolizzeDonut-CR-Cluster2di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K9ac-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6551
- F1 Score: 0.6243
- Accuracy: 0.6236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6594 | 18.18 | 200 | 0.6677 | 0.5961 | 0.6060 |
| 0.6027 | 36.36 | 400 | 0.6681 | 0.6205 | 0.6200 |
| 0.5813 | 54.55 | 600 | 0.6739 | 0.6093 | 0.6085 |
| 0.559 | 72.73 | 800 | 0.6786 | 0.6139 | 0.6132 |
| 0.5395 | 90.91 | 1000 | 0.7065 | 0.6076 | 0.6074 |
| 0.5236 | 109.09 | 1200 | 0.6963 | 0.6114 | 0.6107 |
| 0.5137 | 127.27 | 1400 | 0.6974 | 0.6084 | 0.6081 |
| 0.5022 | 145.45 | 1600 | 0.7297 | 0.6069 | 0.6063 |
| 0.4952 | 163.64 | 1800 | 0.7105 | 0.6090 | 0.6089 |
| 0.4874 | 181.82 | 2000 | 0.7197 | 0.6156 | 0.6157 |
| 0.4797 | 200.0 | 2200 | 0.7246 | 0.6118 | 0.6110 |
| 0.4747 | 218.18 | 2400 | 0.7581 | 0.6095 | 0.6114 |
| 0.4666 | 236.36 | 2600 | 0.7731 | 0.6098 | 0.6092 |
| 0.4612 | 254.55 | 2800 | 0.7770 | 0.6113 | 0.6110 |
| 0.456 | 272.73 | 3000 | 0.7841 | 0.6113 | 0.6107 |
| 0.45 | 290.91 | 3200 | 0.7672 | 0.6080 | 0.6074 |
| 0.4448 | 309.09 | 3400 | 0.7653 | 0.6140 | 0.6132 |
| 0.4383 | 327.27 | 3600 | 0.7769 | 0.6107 | 0.6099 |
| 0.4318 | 345.45 | 3800 | 0.7755 | 0.6120 | 0.6121 |
| 0.4266 | 363.64 | 4000 | 0.8042 | 0.6127 | 0.6125 |
| 0.4205 | 381.82 | 4200 | 0.8200 | 0.6070 | 0.6063 |
| 0.4147 | 400.0 | 4400 | 0.8204 | 0.6097 | 0.6089 |
| 0.4076 | 418.18 | 4600 | 0.8085 | 0.6077 | 0.6071 |
| 0.4017 | 436.36 | 4800 | 0.8466 | 0.6107 | 0.6099 |
| 0.3953 | 454.55 | 5000 | 0.8483 | 0.6099 | 0.6110 |
| 0.3901 | 472.73 | 5200 | 0.8625 | 0.6092 | 0.6085 |
| 0.3846 | 490.91 | 5400 | 0.8448 | 0.6147 | 0.6139 |
| 0.3788 | 509.09 | 5600 | 0.8389 | 0.6068 | 0.6081 |
| 0.3742 | 527.27 | 5800 | 0.8535 | 0.6151 | 0.6146 |
| 0.3686 | 545.45 | 6000 | 0.8849 | 0.6122 | 0.6114 |
| 0.3645 | 563.64 | 6200 | 0.8919 | 0.6084 | 0.6096 |
| 0.3608 | 581.82 | 6400 | 0.9158 | 0.6078 | 0.6071 |
| 0.3559 | 600.0 | 6600 | 0.8738 | 0.6063 | 0.6056 |
| 0.3513 | 618.18 | 6800 | 0.9092 | 0.6110 | 0.6103 |
| 0.3498 | 636.36 | 7000 | 0.8977 | 0.6111 | 0.6107 |
| 0.3465 | 654.55 | 7200 | 0.9368 | 0.6071 | 0.6063 |
| 0.3419 | 672.73 | 7400 | 0.9155 | 0.6032 | 0.6024 |
| 0.3395 | 690.91 | 7600 | 0.9223 | 0.6055 | 0.6056 |
| 0.3363 | 709.09 | 7800 | 0.9243 | 0.6068 | 0.6063 |
| 0.3348 | 727.27 | 8000 | 0.9473 | 0.6100 | 0.6092 |
| 0.3331 | 745.45 | 8200 | 0.9357 | 0.6068 | 0.6063 |
| 0.331 | 763.64 | 8400 | 0.9291 | 0.6051 | 0.6045 |
| 0.3276 | 781.82 | 8600 | 0.9371 | 0.6060 | 0.6056 |
| 0.3292 | 800.0 | 8800 | 0.9336 | 0.6082 | 0.6074 |
| 0.3252 | 818.18 | 9000 | 0.9466 | 0.6083 | 0.6078 |
| 0.3235 | 836.36 | 9200 | 0.9414 | 0.6113 | 0.6107 |
| 0.3229 | 854.55 | 9400 | 0.9442 | 0.6110 | 0.6103 |
| 0.3217 | 872.73 | 9600 | 0.9465 | 0.6134 | 0.6128 |
| 0.3232 | 890.91 | 9800 | 0.9405 | 0.6124 | 0.6117 |
| 0.3216 | 909.09 | 10000 | 0.9452 | 0.6127 | 0.6121 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T07:24:04+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_EMP\_H3K9ac-seqsight\_16384\_512\_22M-L32\_all
===================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_EMP\_H3K9ac dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6551
* F1 Score: 0.6243
* Accuracy: 0.6236
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multilingual-intent-classifier-final-distilbert
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1309
- Accuracy: 0.9777
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1319 | 1.0 | 97069 | 0.1563 | 0.9666 |
| 0.0874 | 2.0 | 194138 | 0.1205 | 0.9751 |
| 0.0473 | 3.0 | 291207 | 0.1309 | 0.9777 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "multilingual-intent-classifier-final-distilbert", "results": []}]} | Mukalingam0813/multilingual-intent-classifier-final-distilbert | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:26:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| multilingual-intent-classifier-final-distilbert
===============================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1309
* Accuracy: 0.9777
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 1.11.0+cu113\n* Datasets 2.3.2\n* Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 1.11.0+cu113\n* Datasets 2.3.2\n* Tokenizers 0.12.1"
] |
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | darrel999/Qwen-7B-Chat_ZD | null | [
"transformers",
"safetensors",
"qwen",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | null | 2024-04-16T07:27:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #qwen #feature-extraction #custom_code #arxiv-1910.09700 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
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This model has been pushed to the Hub using ****:
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This model has been pushed to the Hub using :
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null | transformers |
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| {"library_name": "transformers", "tags": []} | yashas06/Logs | null | [
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
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null | transformers |
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| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-xsum_v2_trained_on_500 | null | [
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# Model Card for Model ID
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null | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/Llama-2-7b-chat-hf_adapters_en.layer1_8_torch.bfloat16_16_32_0.01_2_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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null | transformers |
# Uploaded model
- **Developed by:** NavyaAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/LlamaAdapter-llama2-happy-1000-temp-new | null | [
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"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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text-generation | transformers |
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | luuluumando/new_subnet_6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:33:07+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# model_merge
model_merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)
* [knowledgator/IUPAC2SMILES-canonical-base](https://huggingface.co/knowledgator/IUPAC2SMILES-canonical-base)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: google/flan-t5-base
layer_range: [0, 32]
- model: knowledgator/IUPAC2SMILES-canonical-base
layer_range: [0, 32]
merge_method: slerp
base_model: google/flan-t5-base
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "nagayama0706/model_merge"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "google/flan-t5-base", "knowledgator/IUPAC2SMILES-canonical-base"], "base_model": ["google/flan-t5-base", "knowledgator/IUPAC2SMILES-canonical-base"]} | nagayama0706/model_merge | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"google/flan-t5-base",
"knowledgator/IUPAC2SMILES-canonical-base",
"base_model:google/flan-t5-base",
"base_model:knowledgator/IUPAC2SMILES-canonical-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:34:12+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #google/flan-t5-base #knowledgator/IUPAC2SMILES-canonical-base #base_model-google/flan-t5-base #base_model-knowledgator/IUPAC2SMILES-canonical-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# model_merge
model_merge is a merge of the following models using LazyMergekit:
* google/flan-t5-base
* knowledgator/IUPAC2SMILES-canonical-base
## Configuration
## Usage
| [
"# model_merge\n\nmodel_merge is a merge of the following models using LazyMergekit:\n* google/flan-t5-base\n* knowledgator/IUPAC2SMILES-canonical-base",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #google/flan-t5-base #knowledgator/IUPAC2SMILES-canonical-base #base_model-google/flan-t5-base #base_model-knowledgator/IUPAC2SMILES-canonical-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# model_merge\n\nmodel_merge is a merge of the following models using LazyMergekit:\n* google/flan-t5-base\n* knowledgator/IUPAC2SMILES-canonical-base",
"## Configuration",
"## Usage"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me3-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8204
- F1 Score: 0.5529
- Accuracy: 0.5554
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6876 | 13.33 | 200 | 0.6851 | 0.5248 | 0.5524 |
| 0.6551 | 26.67 | 400 | 0.7065 | 0.5433 | 0.5435 |
| 0.6366 | 40.0 | 600 | 0.7208 | 0.5398 | 0.5416 |
| 0.6206 | 53.33 | 800 | 0.7382 | 0.5403 | 0.5424 |
| 0.6055 | 66.67 | 1000 | 0.7504 | 0.5456 | 0.5459 |
| 0.5951 | 80.0 | 1200 | 0.7404 | 0.5409 | 0.5424 |
| 0.5883 | 93.33 | 1400 | 0.7659 | 0.5327 | 0.5418 |
| 0.5807 | 106.67 | 1600 | 0.7577 | 0.5431 | 0.5437 |
| 0.5773 | 120.0 | 1800 | 0.7617 | 0.5428 | 0.5467 |
| 0.5726 | 133.33 | 2000 | 0.7777 | 0.5412 | 0.5421 |
| 0.5675 | 146.67 | 2200 | 0.7910 | 0.5430 | 0.5478 |
| 0.5638 | 160.0 | 2400 | 0.7718 | 0.5370 | 0.5476 |
| 0.5595 | 173.33 | 2600 | 0.7956 | 0.5380 | 0.5459 |
| 0.5549 | 186.67 | 2800 | 0.7839 | 0.5390 | 0.5454 |
| 0.5513 | 200.0 | 3000 | 0.7908 | 0.5394 | 0.5437 |
| 0.547 | 213.33 | 3200 | 0.7933 | 0.5432 | 0.5437 |
| 0.5413 | 226.67 | 3400 | 0.8055 | 0.5473 | 0.5495 |
| 0.5367 | 240.0 | 3600 | 0.7990 | 0.5489 | 0.5516 |
| 0.5315 | 253.33 | 3800 | 0.8072 | 0.5516 | 0.5541 |
| 0.5265 | 266.67 | 4000 | 0.7990 | 0.5514 | 0.5530 |
| 0.5228 | 280.0 | 4200 | 0.8221 | 0.5563 | 0.5598 |
| 0.5151 | 293.33 | 4400 | 0.8487 | 0.5473 | 0.5489 |
| 0.5109 | 306.67 | 4600 | 0.8232 | 0.5460 | 0.5462 |
| 0.5048 | 320.0 | 4800 | 0.8249 | 0.5488 | 0.5484 |
| 0.4995 | 333.33 | 5000 | 0.8387 | 0.5484 | 0.5533 |
| 0.4957 | 346.67 | 5200 | 0.8413 | 0.5486 | 0.5524 |
| 0.4915 | 360.0 | 5400 | 0.8302 | 0.5514 | 0.5516 |
| 0.4857 | 373.33 | 5600 | 0.8529 | 0.5477 | 0.5527 |
| 0.4807 | 386.67 | 5800 | 0.8355 | 0.5464 | 0.5465 |
| 0.4785 | 400.0 | 6000 | 0.8530 | 0.5524 | 0.5533 |
| 0.4724 | 413.33 | 6200 | 0.8700 | 0.5535 | 0.5538 |
| 0.4685 | 426.67 | 6400 | 0.8895 | 0.5529 | 0.5546 |
| 0.4646 | 440.0 | 6600 | 0.8700 | 0.5501 | 0.55 |
| 0.4619 | 453.33 | 6800 | 0.8619 | 0.5530 | 0.5543 |
| 0.4571 | 466.67 | 7000 | 0.8729 | 0.5517 | 0.5538 |
| 0.4534 | 480.0 | 7200 | 0.8587 | 0.5520 | 0.5530 |
| 0.45 | 493.33 | 7400 | 0.8706 | 0.5547 | 0.5560 |
| 0.4491 | 506.67 | 7600 | 0.8734 | 0.5489 | 0.5508 |
| 0.4455 | 520.0 | 7800 | 0.8879 | 0.5493 | 0.5503 |
| 0.444 | 533.33 | 8000 | 0.8704 | 0.5531 | 0.5549 |
| 0.442 | 546.67 | 8200 | 0.8951 | 0.5561 | 0.5571 |
| 0.4397 | 560.0 | 8400 | 0.8816 | 0.5541 | 0.5557 |
| 0.4364 | 573.33 | 8600 | 0.8912 | 0.5532 | 0.5546 |
| 0.4365 | 586.67 | 8800 | 0.8867 | 0.5519 | 0.5522 |
| 0.434 | 600.0 | 9000 | 0.8886 | 0.5528 | 0.5535 |
| 0.4328 | 613.33 | 9200 | 0.8905 | 0.5544 | 0.5571 |
| 0.4312 | 626.67 | 9400 | 0.8939 | 0.5519 | 0.5530 |
| 0.4306 | 640.0 | 9600 | 0.8907 | 0.5514 | 0.5527 |
| 0.4293 | 653.33 | 9800 | 0.8979 | 0.5555 | 0.5571 |
| 0.4297 | 666.67 | 10000 | 0.8950 | 0.5541 | 0.5557 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T07:35:57+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_EMP\_H3K4me3-seqsight\_16384\_512\_22M-L32\_all
====================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8204
* F1 Score: 0.5529
* Accuracy: 0.5554
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual inversion text2image fine-tuning - nemod/textual_inversion_cat_toy_sdxl_paper_batch1
These are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "diffusers-training", "textual_inversion"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "inference": true} | nemod/textual_inversion_cat_toy_sdxl_paper_batch1 | null | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"diffusers-training",
"textual_inversion",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-16T07:37:45+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #diffusers-training #textual_inversion #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us
|
# Textual inversion text2image fine-tuning - nemod/textual_inversion_cat_toy_sdxl_paper_batch1
These are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | [
"# Textual inversion text2image fine-tuning - nemod/textual_inversion_cat_toy_sdxl_paper_batch1\nThese are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
"TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #diffusers-training #textual_inversion #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n",
"# Textual inversion text2image fine-tuning - nemod/textual_inversion_cat_toy_sdxl_paper_batch1\nThese are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/dumbo-krillin11 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:37:56+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
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"### Training Data",
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"### Model Architecture and Objective",
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"## Glossary [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine_tuned_roberta
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3144
- F1: 0.44
- F5: 0.5013
- Precision: 0.3333
- Recall: 0.6471
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F5 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
| No log | 1.0 | 9 | 0.3437 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 18 | 0.3128 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 27 | 0.3089 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 4.0 | 36 | 0.3058 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 5.0 | 45 | 0.2960 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 6.0 | 54 | 0.2744 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 7.0 | 63 | 0.2674 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 8.0 | 72 | 0.2744 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 9.0 | 81 | 0.2745 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 10.0 | 90 | 0.2849 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 11.0 | 99 | 0.3079 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 12.0 | 108 | 0.2686 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 13.0 | 117 | 0.2856 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 14.0 | 126 | 0.3047 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 15.0 | 135 | 0.2697 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 16.0 | 144 | 0.2783 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 17.0 | 153 | 0.2816 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 18.0 | 162 | 0.2660 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 19.0 | 171 | 0.3168 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 20.0 | 180 | 0.2796 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 21.0 | 189 | 0.2956 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 22.0 | 198 | 0.2610 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 23.0 | 207 | 0.2680 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 24.0 | 216 | 0.2836 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 25.0 | 225 | 0.2841 | 0.0588 | 0.0499 | 0.1111 | 0.04 |
| No log | 26.0 | 234 | 0.3023 | 0.2128 | 0.2077 | 0.2273 | 0.2 |
| No log | 27.0 | 243 | 0.3097 | 0.2903 | 0.3135 | 0.2432 | 0.36 |
| No log | 28.0 | 252 | 0.3118 | 0.2963 | 0.3049 | 0.2759 | 0.32 |
| No log | 29.0 | 261 | 0.3110 | 0.3256 | 0.3065 | 0.3889 | 0.28 |
| No log | 30.0 | 270 | 0.3261 | 0.2927 | 0.2700 | 0.375 | 0.24 |
| No log | 31.0 | 279 | 0.3167 | 0.4 | 0.4144 | 0.3667 | 0.44 |
| No log | 32.0 | 288 | 0.2997 | 0.2857 | 0.2663 | 0.3529 | 0.24 |
| No log | 33.0 | 297 | 0.3202 | 0.3385 | 0.3712 | 0.275 | 0.44 |
| No log | 34.0 | 306 | 0.2841 | 0.3922 | 0.3951 | 0.3846 | 0.4 |
| No log | 35.0 | 315 | 0.3986 | 0.4048 | 0.4788 | 0.2881 | 0.68 |
| No log | 36.0 | 324 | 0.2881 | 0.4082 | 0.4050 | 0.4167 | 0.4 |
| No log | 37.0 | 333 | 0.2719 | 0.3500 | 0.3195 | 0.4667 | 0.28 |
| No log | 38.0 | 342 | 0.3239 | 0.4615 | 0.5062 | 0.375 | 0.6 |
| No log | 39.0 | 351 | 0.2652 | 0.4906 | 0.5014 | 0.4643 | 0.52 |
| No log | 40.0 | 360 | 0.2813 | 0.5 | 0.5213 | 0.4516 | 0.56 |
| No log | 41.0 | 369 | 0.3664 | 0.4507 | 0.5081 | 0.3478 | 0.64 |
| No log | 42.0 | 378 | 0.2577 | 0.4651 | 0.4379 | 0.5556 | 0.4 |
| No log | 43.0 | 387 | 0.3193 | 0.4928 | 0.5507 | 0.3864 | 0.68 |
| No log | 44.0 | 396 | 0.2627 | 0.4615 | 0.4684 | 0.4444 | 0.48 |
| No log | 45.0 | 405 | 0.2850 | 0.4643 | 0.4841 | 0.4194 | 0.52 |
| No log | 46.0 | 414 | 0.2971 | 0.5574 | 0.5986 | 0.4722 | 0.68 |
| No log | 47.0 | 423 | 0.2804 | 0.5185 | 0.5336 | 0.4828 | 0.56 |
| No log | 48.0 | 432 | 0.2845 | 0.5091 | 0.5274 | 0.4667 | 0.56 |
| No log | 49.0 | 441 | 0.3005 | 0.5312 | 0.5797 | 0.4359 | 0.68 |
| No log | 50.0 | 450 | 0.2981 | 0.5079 | 0.5514 | 0.4211 | 0.64 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0a0+ebedce2
- Datasets 2.17.1
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "fine_tuned_roberta", "results": []}]} | mehdie/fine_tuned_XLM-roberta | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:38:31+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| fine\_tuned\_roberta
====================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3144
* F1: 0.44
* F5: 0.5013
* Precision: 0.3333
* Recall: 0.6471
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 256
* eval\_batch\_size: 256
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.3.0a0+ebedce2
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-rocstories
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5423
- Accuracy: 0.8064
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 299 | 0.6900 | 0.5526 |
| 0.6828 | 2.0 | 598 | 0.6062 | 0.6654 |
| 0.6828 | 3.0 | 897 | 0.5321 | 0.7632 |
| 0.401 | 4.0 | 1196 | 0.5423 | 0.8064 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-cased", "model-index": [{"name": "distilbert-base-cased-rocstories", "results": []}]} | KeiHeityuu/distilbert-base-cased-rocstories | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:38:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-cased-rocstories
================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5423
* Accuracy: 0.8064
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ruBert-base-sberquad-0.01-len_4-filtered-v2
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-sberquad-0.01-len_4-filtered-v2", "results": []}]} | Shalazary/ruBert-base-sberquad-0.01-len_4-filtered-v2 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T07:39:15+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us
|
# ruBert-base-sberquad-0.01-len_4-filtered-v2
This model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ruBert-base-sberquad-0.005-len_4-filtered-negative
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-sberquad-0.005-len_4-filtered-negative", "results": []}]} | Shalazary/ruBert-base-sberquad-0.005-len_4-filtered-negative | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T07:41:29+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us
|
# ruBert-base-sberquad-0.005-len_4-filtered-negative
This model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 5000",
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 5000",
"### Training results",
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | bluedoumi/llama-2-7b-chat-hkcode-kor | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-16T07:41:51+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-cnn_v2_trained_on_10 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:42:51+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-to-image | diffusers |
# Textual inversion text2image fine-tuning - EmXCaesar/textual_inversion_airplane
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "textual_inversion"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true} | EmXCaesar/textual_inversion_airplane | null | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-16T07:43:06+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# Textual inversion text2image fine-tuning - EmXCaesar/textual_inversion_airplane
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| [
"# Textual inversion text2image fine-tuning - EmXCaesar/textual_inversion_airplane\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
"TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# Textual inversion text2image fine-tuning - EmXCaesar/textual_inversion_airplane\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wev2vec-reg-ben
This model is a fine-tuned version of [Sameen53/cv_bn_bestModel_1](https://huggingface.co/Sameen53/cv_bn_bestModel_1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2706
- Wer: 0.3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 0.4297 | 3.12 | 50 | 0.2821 | 0.3 |
| 0.4125 | 6.25 | 100 | 0.2706 | 0.3 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "wev2vec-reg-ben", "results": []}]} | Nasiat/wev2vec-reg-ben | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:44:03+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wev2vec-reg-ben
===============
This model is a fine-tuned version of Sameen53/cv\_bn\_bestModel\_1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2706
* Wer: 0.3
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-07
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 7
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 7\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.20.1\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 7\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.20.1\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-cnn_v2_trained_on_25 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:44:03+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-CR-Cluster3di7-ConPre-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-CR-Cluster3di7-ConPre-3Epochs", "results": []}]} | tedad09/PolizzeDonut-CR-Cluster3di7-ConPre-3Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:44:10+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 #license-mit #endpoints_compatible #region-us
|
# PolizzeDonut-CR-Cluster3di7-ConPre-3Epochs
This model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# PolizzeDonut-CR-Cluster3di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
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"# PolizzeDonut-CR-Cluster3di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | aslawliet/quietstar | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:44:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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null | diffusers |
# Model Card for Model ID
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "diffusers"} | cheungra/ocularaid-diffusion | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2024-04-16T07:46:15+00:00 | [
"1910.09700"
] | [] | TAGS
#diffusers #safetensors #arxiv-1910.09700 #region-us
|
# Model Card for Model ID
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## Environmental Impact
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- Carbon Emitted:
## Technical Specifications [optional]
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[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Authors [optional]
## Model Card Contact
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null | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-cnn_v2_trained_on_50 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:46:17+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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### Recommendations
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## Evaluation
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#### Factors
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### Results
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- Compute Region:
- Carbon Emitted:
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APA:
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# model_merge2
model_merge2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)
* [google/mt5-base](https://huggingface.co/google/mt5-base)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: google/flan-t5-base
layer_range: [0, 32]
- model: google/mt5-base
layer_range: [0, 32]
merge_method: slerp
base_model: google/flan-t5-base
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "nagayama0706/model_merge2"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "google/flan-t5-base", "google/mt5-base"], "base_model": ["google/flan-t5-base", "google/mt5-base"]} | nagayama0706/model_merge2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"google/flan-t5-base",
"google/mt5-base",
"base_model:google/flan-t5-base",
"base_model:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:46:37+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #google/flan-t5-base #google/mt5-base #base_model-google/flan-t5-base #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# model_merge2
model_merge2 is a merge of the following models using LazyMergekit:
* google/flan-t5-base
* google/mt5-base
## Configuration
## Usage
| [
"# model_merge2\n\nmodel_merge2 is a merge of the following models using LazyMergekit:\n* google/flan-t5-base\n* google/mt5-base",
"## Configuration",
"## Usage"
] | [
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"# model_merge2\n\nmodel_merge2 is a merge of the following models using LazyMergekit:\n* google/flan-t5-base\n* google/mt5-base",
"## Configuration",
"## Usage"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small ATC - ATCText
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ATC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2486
- Wer: 10.6129
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2533 | 0.42 | 1000 | 0.3465 | 16.2868 |
| 0.235 | 0.84 | 2000 | 0.2881 | 13.5237 |
| 0.0851 | 1.27 | 3000 | 0.2607 | 10.6048 |
| 0.1317 | 1.69 | 4000 | 0.2486 | 10.6129 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mShiry/ATC_combined"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small ATC - ATCText", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "ATC", "type": "mShiry/ATC_combined", "args": "split: test"}, "metrics": [{"type": "wer", "value": 10.612930650580948, "name": "Wer"}]}]}]} | san2003m/whisper-small-atc | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:mShiry/ATC_combined",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:47:57+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-mShiry/ATC_combined #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Small ATC - ATCText
===========================
This model is a fine-tuned version of openai/whisper-small on the ATC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2486
* Wer: 10.6129
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000",
"### Training results",
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] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | MayukhHaldar/Videhack-Standard-V-1-0-1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:47:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-to-image | diffusers |
# BRIA-2.3-ControlNet-Background-Generation, Model Card
BRIA 2.3 ControlNet-Background Generation, trained on the foundation of [BRIA 2.3 Text-to-Image](https://huggingface.co/briaai/BRIA-2.3), enables the generation of high-quality images guided by a textual prompt and the extracted background mask estimation from an input image. This allows for the creation of different background variations of an image, all sharing the same foreground.
[BRIA 2.3](https://huggingface.co/briaai/BRIA-2.3) was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
### Model Description
- **Developed by:** BRIA AI
- **Model type:** [ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet) for Latent diffusion
- **License:** [bria-2.3](https://bria.ai/bria-huggingface-model-license-agreement/)
- **Model Description:** ControlNet Background-Generation for BRIA 2.3 Text-to-Image model. The model generates images guided by text and the background mask.
- **Resources for more information:** [BRIA AI](https://bria.ai/)
### Get Access
BRIA 2.3 ControlNet-Background Generation requires access to BRIA 2.3 Text-to-Image. For more information, [click here](https://huggingface.co/briaai/BRIA-2.3).
## Usage
Installation
```bash
pip install -qr https://huggingface.co/briaai/BRIA-2.3-ControlNet-BG-Gen/resolve/main/requirements.txt
huggingface-cli download briaai/BRIA-2.3-ControlNet-BG-Gen --include replace_bg/* --local-dir . --quiet
```
```python
import torch
from diffusers import (
AutoencoderKL,
EulerAncestralDiscreteScheduler,
)
from diffusers.utils import load_image
from replace_bg.model.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline
from replace_bg.model.controlnet import ControlNetModel
from replace_bg.utilities import resize_image, remove_bg_from_image, paste_fg_over_image, get_control_image_tensor
controlnet = ControlNetModel.from_pretrained("briaai/BRIA-2.3-ControlNet-BG-Gen", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16, vae=vae).to('cuda:0')
pipe.scheduler = EulerAncestralDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
steps_offset=1
)
image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
image = load_image(image_path)
image = resize_image(image)
mask = remove_bg_from_image(image_path)
control_tensor = get_control_image_tensor(pipe.vae, image, mask)
prompt = "in a zoo"
negative_prompt = "Logo,Watermark,Text,Ugly,Bad proportions,Bad quality,Out of frame,Mutation"
generator = torch.Generator(device="cuda:0").manual_seed(0)
gen_img = pipe(
negative_prompt=negative_prompt,
prompt=prompt,
controlnet_conditioning_scale=1.0,
num_inference_steps=50,
image = control_tensor,
generator=generator
).images[0]
result_image = paste_fg_over_image(gen_img, image, mask)
```
| {"license": "other", "tags": ["text-to-image", "controlnet model", "legal liability", "commercial use"], "license_name": "bria-2.3", "license_link": "https://bria.ai/bria-huggingface-model-license-agreement/", "inference": false, "extra_gated_prompt": "This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}} | briaai/BRIA-2.3-ControlNet-BG-Gen | null | [
"diffusers",
"safetensors",
"text-to-image",
"controlnet model",
"legal liability",
"commercial use",
"license:other",
"has_space",
"region:us"
] | null | 2024-04-16T07:48:38+00:00 | [] | [] | TAGS
#diffusers #safetensors #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #region-us
|
# BRIA-2.3-ControlNet-Background-Generation, Model Card
BRIA 2.3 ControlNet-Background Generation, trained on the foundation of BRIA 2.3 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted background mask estimation from an input image. This allows for the creation of different background variations of an image, all sharing the same foreground.
BRIA 2.3 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
!examples
### Model Description
- Developed by: BRIA AI
- Model type: ControlNet for Latent diffusion
- License: bria-2.3
- Model Description: ControlNet Background-Generation for BRIA 2.3 Text-to-Image model. The model generates images guided by text and the background mask.
- Resources for more information: BRIA AI
### Get Access
BRIA 2.3 ControlNet-Background Generation requires access to BRIA 2.3 Text-to-Image. For more information, click here.
## Usage
Installation
| [
"# BRIA-2.3-ControlNet-Background-Generation, Model Card \n\n\nBRIA 2.3 ControlNet-Background Generation, trained on the foundation of BRIA 2.3 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted background mask estimation from an input image. This allows for the creation of different background variations of an image, all sharing the same foreground.\n\n\nBRIA 2.3 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!examples",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.3\n\n- Model Description: ControlNet Background-Generation for BRIA 2.3 Text-to-Image model. The model generates images guided by text and the background mask.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.3 ControlNet-Background Generation requires access to BRIA 2.3 Text-to-Image. For more information, click here.",
"## Usage\nInstallation"
] | [
"TAGS\n#diffusers #safetensors #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #region-us \n",
"# BRIA-2.3-ControlNet-Background-Generation, Model Card \n\n\nBRIA 2.3 ControlNet-Background Generation, trained on the foundation of BRIA 2.3 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted background mask estimation from an input image. This allows for the creation of different background variations of an image, all sharing the same foreground.\n\n\nBRIA 2.3 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!examples",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.3\n\n- Model Description: ControlNet Background-Generation for BRIA 2.3 Text-to-Image model. The model generates images guided by text and the background mask.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.3 ControlNet-Background Generation requires access to BRIA 2.3 Text-to-Image. For more information, click here.",
"## Usage\nInstallation"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ft_wav2vec2_base_six_1000
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4818
- Wer: 40.2628
- Cer: 18.6678
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|
| 3.2787 | 22.73 | 1000 | 1.1585 | 59.3958 | 25.8494 |
| 0.4164 | 45.45 | 2000 | 1.2611 | 44.7175 | 20.5846 |
| 0.144 | 68.18 | 3000 | 1.3470 | 42.6865 | 19.8831 |
| 0.0774 | 90.91 | 4000 | 1.4210 | 41.7307 | 19.1922 |
| 0.05 | 113.64 | 5000 | 1.4818 | 40.2628 | 18.6678 |
### Framework versions
- Transformers 4.39.3
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "ft_wav2vec2_base_six_1000", "results": []}]} | ImanNalia/ft_wav2vec2_base_six_1000 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:49:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
| ft\_wav2vec2\_base\_six\_1000
=============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4818
* Wer: 40.2628
* Cer: 18.6678
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* training\_steps: 5000
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* training\\_steps: 5000",
"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* training\\_steps: 5000",
"### Training results",
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dist5-small-laShoot
This model is a fine-tuned version of DisT5 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google-t5/t5-small", "model-index": [{"name": "Dist5-small-laShoot", "results": []}]} | Piyyussh/Dist5-small-laShoot | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:50:07+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-google-t5/t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dist5-small-laShoot
This model is a fine-tuned version of DisT5 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# Dist5-small-laShoot\n\nThis model is a fine-tuned version of DisT5 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6627",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
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"# Dist5-small-laShoot\n\nThis model is a fine-tuned version of DisT5 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6627",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
feature-extraction | transformers | # pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564
## Model Description
pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564 is a fine-tuned version of BAAI/bge-small-en-v1.5 designed for a specific domain.
## Use Case
This model is designed to support various applications in natural language processing and understanding.
## Associated Dataset
This the dataset for this model can be found [**here**](https://huggingface.co/datasets/florianhoenicke/pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564).
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from transformers import AutoModel, AutoTokenizer
llm_name = "pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564"
tokenizer = AutoTokenizer.from_pretrained(llm_name)
model = AutoModel.from_pretrained(llm_name)
tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
```
| {} | florianhoenicke/pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564 | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:50:12+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #feature-extraction #endpoints_compatible #region-us
| # pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564
## Model Description
pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564 is a fine-tuned version of BAAI/bge-small-en-v1.5 designed for a specific domain.
## Use Case
This model is designed to support various applications in natural language processing and understanding.
## Associated Dataset
This the dataset for this model can be found here.
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
| [
"# pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564",
"## Model Description\n\npet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564 is a fine-tuned version of BAAI/bge-small-en-v1.5 designed for a specific domain.",
"## Use Case\nThis model is designed to support various applications in natural language processing and understanding.",
"## Associated Dataset\n\nThis the dataset for this model can be found here.",
"## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:"
] | [
"TAGS\n#transformers #safetensors #bert #feature-extraction #endpoints_compatible #region-us \n",
"# pet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564",
"## Model Description\n\npet-shop-1000-64-16-BAAI_bge-small-en-v1.5-1000_9062874564 is a fine-tuned version of BAAI/bge-small-en-v1.5 designed for a specific domain.",
"## Use Case\nThis model is designed to support various applications in natural language processing and understanding.",
"## Associated Dataset\n\nThis the dataset for this model can be found here.",
"## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-cnn_v2_trained_on_100 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:50:29+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
video-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# timesformer-base-finetuned-k400-finetuned-ElderReact-anger-balanced
This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8048
- Accuracy: 0.5012
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 240
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6923 | 0.1 | 25 | 0.8338 | 0.4601 |
| 0.6436 | 1.1 | 50 | 0.9187 | 0.5058 |
| 0.5464 | 2.1 | 75 | 0.8165 | 0.5313 |
| 0.4147 | 3.1 | 100 | 0.9950 | 0.5228 |
| 0.4483 | 4.1 | 125 | 1.0642 | 0.5080 |
| 0.3565 | 5.1 | 150 | 0.8611 | 0.5143 |
| 0.3114 | 6.1 | 175 | 0.9595 | 0.5186 |
| 0.3074 | 7.1 | 200 | 1.0242 | 0.5197 |
| 0.2888 | 8.1 | 225 | 1.1212 | 0.5069 |
| 0.2609 | 9.06 | 240 | 1.1119 | 0.5112 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/timesformer-base-finetuned-k400", "model-index": [{"name": "timesformer-base-finetuned-k400-finetuned-ElderReact-anger-balanced", "results": []}]} | minhah/timesformer-base-finetuned-k400-finetuned-ElderReact-anger-balanced | null | [
"transformers",
"safetensors",
"timesformer",
"video-classification",
"generated_from_trainer",
"base_model:facebook/timesformer-base-finetuned-k400",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:51:38+00:00 | [] | [] | TAGS
#transformers #safetensors #timesformer #video-classification #generated_from_trainer #base_model-facebook/timesformer-base-finetuned-k400 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| timesformer-base-finetuned-k400-finetuned-ElderReact-anger-balanced
===================================================================
This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8048
* Accuracy: 0.5012
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 240
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.1.0+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 240",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 240",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Mixtral-8x7B-Instruct-v0.1-japanese-alpha
Mixtral-8x7B-Instruct-v0.1-japanese-alphaは[Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)をベースに日本語の語彙拡張継続事前学習を実施した学習途中のモデルです。
[ABEJAのテックブログ](https://tech-blog.abeja.asia/entry/abeja-nedo-project-part1-202404)にて評価を実施した途中結果モデルとして公開しています。
学習を実施したMetagton-LMのレポジトリは[こちら](https://github.com/abeja-inc/Megatron-LM)です。
# 使い方
``` python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "abeja/Mixtral-8x7B-Instruct-v0.1-japanese-alpha"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
use_cache=True,
device_map="auto",
)
model.eval()
input_text = """# system
誠実で紳士的で優秀なAIアシスタントとして、簡潔でわかりやすく役に立つ回答を自信をもって答えなさい。
# question
人とAIが協調するためには？
# answer"""
input_ids = tokenizer.encode(input_text, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
input_ids.to(model.device),
max_new_tokens=256,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
output = tokenizer.decode(output_ids.tolist()[0], skip_special_tokens=True)
print(output)
```
# 開発者
- Keisuke Fujimoto
- Kentaro Nakanishi
- Kyo Hattori
- Shinya Otani
- Shogo Muranushi
(*)アルファベット順
| {"language": ["ja"], "license": "apache-2.0", "widget": [{"text": "\u4eba\u3068AI\u304c\u5354\u8abf\u3059\u308b\u305f\u3081\u306b\u306f\u3001"}]} | abeja/Mixtral-8x7B-Instruct-v0.1-japanese-alpha | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:52:50+00:00 | [] | [
"ja"
] | TAGS
#transformers #safetensors #mixtral #text-generation #ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mixtral-8x7B-Instruct-v0.1-japanese-alpha
Mixtral-8x7B-Instruct-v0.1-japanese-alphaはMixtral-8x7B-Instruct-v0.1をベースに日本語の語彙拡張継続事前学習を実施した学習途中のモデルです。
ABEJAのテックブログにて評価を実施した途中結果モデルとして公開しています。
学習を実施したMetagton-LMのレポジトリはこちらです。
# 使い方
# 開発者
- Keisuke Fujimoto
- Kentaro Nakanishi
- Kyo Hattori
- Shinya Otani
- Shogo Muranushi
(*)アルファベット順
| [
"# Mixtral-8x7B-Instruct-v0.1-japanese-alpha\n\nMixtral-8x7B-Instruct-v0.1-japanese-alphaはMixtral-8x7B-Instruct-v0.1をベースに日本語の語彙拡張継続事前学習を実施した学習途中のモデルです。 \nABEJAのテックブログにて評価を実施した途中結果モデルとして公開しています。 \n学習を実施したMetagton-LMのレポジトリはこちらです。",
"# 使い方",
"# 開発者\n- Keisuke Fujimoto\n- Kentaro Nakanishi\n- Kyo Hattori\n- Shinya Otani\n- Shogo Muranushi \n(*)アルファベット順"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mixtral-8x7B-Instruct-v0.1-japanese-alpha\n\nMixtral-8x7B-Instruct-v0.1-japanese-alphaはMixtral-8x7B-Instruct-v0.1をベースに日本語の語彙拡張継続事前学習を実施した学習途中のモデルです。 \nABEJAのテックブログにて評価を実施した途中結果モデルとして公開しています。 \n学習を実施したMetagton-LMのレポジトリはこちらです。",
"# 使い方",
"# 開発者\n- Keisuke Fujimoto\n- Kentaro Nakanishi\n- Kyo Hattori\n- Shinya Otani\n- Shogo Muranushi \n(*)アルファベット順"
] |
text-generation | transformers |
# Mixtral-8x7B-Instruct-v0.1-japanese-alpha-merged
Mixtral-8x7B-Instruct-v0.1-japanese-alpha-mergedは[Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)をベースに日本語の語彙拡張継続事前学習を実施した[学習途中のモデル](https://huggingface.co/abeja/Mixtral-8x7B-Instruct-v0.1-japanese-alpha)に対して、差分マージを実施したモデルです。
[ABEJAのテックブログ](https://tech-blog.abeja.asia/entry/abeja-nedo-project-part1-202404)にて評価を実施した途中結果モデルとして公開しています。
学習を実施したMetagton-LMのレポジトリは[こちら](https://github.com/abeja-inc/Megatron-LM)です。
# 使い方
``` python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "abeja/Mixtral-8x7B-Instruct-v0.1-japanese-alpha-merged"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
use_cache=True,
device_map="auto",
)
model.eval()
input_text = """# system
誠実で紳士的で優秀なAIアシスタントとして、簡潔でわかりやすく役に立つ回答を自信をもって答えなさい。
# question
人とAIが協調するためには？
# answer"""
input_ids = tokenizer.encode(input_text, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
input_ids.to(model.device),
max_new_tokens=256,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
output = tokenizer.decode(output_ids.tolist()[0], skip_special_tokens=True)
print(output)
```
# 開発者
- Keisuke Fujimoto
- Kentaro Nakanishi
- Kyo Hattori
- Shinya Otani
- Shogo Muranushi
(*)アルファベット順 | {"language": ["ja"], "license": "apache-2.0", "widget": [{"text": "\u4eba\u3068AI\u304c\u5354\u8abf\u3059\u308b\u305f\u3081\u306b\u306f\u3001"}]} | abeja/Mixtral-8x7B-Instruct-v0.1-japanese-alpha-merged | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:54:14+00:00 | [] | [
"ja"
] | TAGS
#transformers #safetensors #mixtral #text-generation #ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mixtral-8x7B-Instruct-v0.1-japanese-alpha-merged
Mixtral-8x7B-Instruct-v0.1-japanese-alpha-mergedはMixtral-8x7B-Instruct-v0.1をベースに日本語の語彙拡張継続事前学習を実施した学習途中のモデルに対して、差分マージを実施したモデルです。
ABEJAのテックブログにて評価を実施した途中結果モデルとして公開しています。
学習を実施したMetagton-LMのレポジトリはこちらです。
# 使い方
# 開発者
- Keisuke Fujimoto
- Kentaro Nakanishi
- Kyo Hattori
- Shinya Otani
- Shogo Muranushi
(*)アルファベット順 | [
"# Mixtral-8x7B-Instruct-v0.1-japanese-alpha-merged\n\nMixtral-8x7B-Instruct-v0.1-japanese-alpha-mergedはMixtral-8x7B-Instruct-v0.1をベースに日本語の語彙拡張継続事前学習を実施した学習途中のモデルに対して、差分マージを実施したモデルです。 \nABEJAのテックブログにて評価を実施した途中結果モデルとして公開しています。 \n学習を実施したMetagton-LMのレポジトリはこちらです。",
"# 使い方",
"# 開発者\n- Keisuke Fujimoto\n- Kentaro Nakanishi\n- Kyo Hattori\n- Shinya Otani\n- Shogo Muranushi \n(*)アルファベット順"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mixtral-8x7B-Instruct-v0.1-japanese-alpha-merged\n\nMixtral-8x7B-Instruct-v0.1-japanese-alpha-mergedはMixtral-8x7B-Instruct-v0.1をベースに日本語の語彙拡張継続事前学習を実施した学習途中のモデルに対して、差分マージを実施したモデルです。 \nABEJAのテックブログにて評価を実施した途中結果モデルとして公開しています。 \n学習を実施したMetagton-LMのレポジトリはこちらです。",
"# 使い方",
"# 開発者\n- Keisuke Fujimoto\n- Kentaro Nakanishi\n- Kyo Hattori\n- Shinya Otani\n- Shogo Muranushi \n(*)アルファベット順"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotions
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: 0.2191
- Accuracy: 0.924
- F1: 0.9242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8388 | 1.0 | 250 | 0.3217 | 0.9025 | 0.9011 |
| 0.2553 | 2.0 | 500 | 0.2191 | 0.924 | 0.9242 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotions", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.924, "name": "Accuracy"}, {"type": "f1", "value": 0.9242323915954499, "name": "F1"}]}]}]} | fibleep/distilbert-base-uncased-finetuned-emotions | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:55:01+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotions
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2191
* Accuracy: 0.924
* F1: 0.9242
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9887
- F1 Score: 0.7491
- Accuracy: 0.7495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1536
- eval_batch_size: 1536
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.59 | 25.0 | 200 | 0.5462 | 0.7210 | 0.7228 |
| 0.4636 | 50.0 | 400 | 0.5709 | 0.7201 | 0.7228 |
| 0.4202 | 75.0 | 600 | 0.5798 | 0.7318 | 0.7310 |
| 0.3795 | 100.0 | 800 | 0.6212 | 0.7366 | 0.7365 |
| 0.3437 | 125.0 | 1000 | 0.6216 | 0.7302 | 0.7303 |
| 0.3223 | 150.0 | 1200 | 0.6566 | 0.7290 | 0.7296 |
| 0.3056 | 175.0 | 1400 | 0.6530 | 0.7285 | 0.7303 |
| 0.2916 | 200.0 | 1600 | 0.6811 | 0.7273 | 0.7290 |
| 0.2798 | 225.0 | 1800 | 0.7118 | 0.7270 | 0.7290 |
| 0.2673 | 250.0 | 2000 | 0.7122 | 0.7370 | 0.7385 |
| 0.2561 | 275.0 | 2200 | 0.7283 | 0.7343 | 0.7365 |
| 0.2429 | 300.0 | 2400 | 0.8025 | 0.7294 | 0.7331 |
| 0.2297 | 325.0 | 2600 | 0.7762 | 0.7421 | 0.7433 |
| 0.219 | 350.0 | 2800 | 0.8199 | 0.7370 | 0.7392 |
| 0.2103 | 375.0 | 3000 | 0.8273 | 0.7383 | 0.7392 |
| 0.1992 | 400.0 | 3200 | 0.8224 | 0.7408 | 0.7413 |
| 0.1903 | 425.0 | 3400 | 0.8409 | 0.7344 | 0.7365 |
| 0.1833 | 450.0 | 3600 | 0.8632 | 0.7379 | 0.7406 |
| 0.1766 | 475.0 | 3800 | 0.8631 | 0.7380 | 0.7385 |
| 0.1695 | 500.0 | 4000 | 0.9297 | 0.7394 | 0.7406 |
| 0.1623 | 525.0 | 4200 | 0.9202 | 0.7383 | 0.7392 |
| 0.1581 | 550.0 | 4400 | 0.8669 | 0.7386 | 0.7399 |
| 0.1501 | 575.0 | 4600 | 0.8859 | 0.7401 | 0.7406 |
| 0.1465 | 600.0 | 4800 | 0.9340 | 0.7458 | 0.7467 |
| 0.141 | 625.0 | 5000 | 0.9413 | 0.7426 | 0.7440 |
| 0.139 | 650.0 | 5200 | 0.9066 | 0.7393 | 0.7413 |
| 0.1328 | 675.0 | 5400 | 0.9567 | 0.7450 | 0.7467 |
| 0.13 | 700.0 | 5600 | 0.9144 | 0.7441 | 0.7447 |
| 0.1278 | 725.0 | 5800 | 0.9503 | 0.7449 | 0.7474 |
| 0.1232 | 750.0 | 6000 | 0.9897 | 0.7436 | 0.7454 |
| 0.1204 | 775.0 | 6200 | 0.9914 | 0.7435 | 0.7454 |
| 0.1182 | 800.0 | 6400 | 0.9820 | 0.7412 | 0.7447 |
| 0.1165 | 825.0 | 6600 | 0.9667 | 0.7432 | 0.7467 |
| 0.1126 | 850.0 | 6800 | 0.9607 | 0.7471 | 0.7481 |
| 0.1121 | 875.0 | 7000 | 1.0055 | 0.7422 | 0.7440 |
| 0.1087 | 900.0 | 7200 | 0.9990 | 0.7449 | 0.7461 |
| 0.1069 | 925.0 | 7400 | 1.0155 | 0.7481 | 0.7495 |
| 0.1043 | 950.0 | 7600 | 1.0001 | 0.7410 | 0.7426 |
| 0.1024 | 975.0 | 7800 | 1.0572 | 0.7455 | 0.7474 |
| 0.1033 | 1000.0 | 8000 | 1.0338 | 0.7407 | 0.7433 |
| 0.1019 | 1025.0 | 8200 | 1.0217 | 0.7475 | 0.7481 |
| 0.1009 | 1050.0 | 8400 | 1.0273 | 0.7433 | 0.7454 |
| 0.0978 | 1075.0 | 8600 | 1.0623 | 0.7475 | 0.7495 |
| 0.099 | 1100.0 | 8800 | 1.0517 | 0.7462 | 0.7481 |
| 0.0958 | 1125.0 | 9000 | 1.0466 | 0.7482 | 0.7502 |
| 0.0962 | 1150.0 | 9200 | 1.0377 | 0.7438 | 0.7461 |
| 0.0957 | 1175.0 | 9400 | 1.0356 | 0.7494 | 0.7509 |
| 0.0959 | 1200.0 | 9600 | 1.0391 | 0.7493 | 0.7509 |
| 0.0943 | 1225.0 | 9800 | 1.0460 | 0.7463 | 0.7481 |
| 0.094 | 1250.0 | 10000 | 1.0476 | 0.7486 | 0.7502 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_EMP_H4-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T07:55:59+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_EMP\_H4-seqsight\_16384\_512\_22M-L32\_all
===============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_EMP\_H4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9887
* F1 Score: 0.7491
* Accuracy: 0.7495
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 1536
* eval\_batch\_size: 1536
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
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] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vecvanilla
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8028
- Wer: 0.3041
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1729 | 1.69 | 100 | 1.0048 | 0.3681 |
| 1.0722 | 3.39 | 200 | 0.9117 | 0.3511 |
| 0.9994 | 5.08 | 300 | 0.9302 | 0.3436 |
| 0.9576 | 6.78 | 400 | 0.8246 | 0.3320 |
| 0.9826 | 8.47 | 500 | 0.7846 | 0.3343 |
| 0.801 | 10.17 | 600 | 0.8600 | 0.3269 |
| 0.8174 | 11.86 | 700 | 0.7871 | 0.3186 |
| 0.7162 | 13.56 | 800 | 0.8164 | 0.3186 |
| 0.7447 | 15.25 | 900 | 0.8965 | 0.3084 |
| 0.6889 | 16.95 | 1000 | 0.8239 | 0.3057 |
| 0.6739 | 18.64 | 1100 | 0.8028 | 0.3041 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-base-960h", "model-index": [{"name": "wav2vecvanilla", "results": []}]} | Chijioke-Mgbahurike/wav2vecvanilla | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:56:49+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-base-960h #license-apache-2.0 #endpoints_compatible #region-us
| wav2vecvanilla
==============
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8028
* Wer: 0.3041
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 20
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-rocstories
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0918
- Accuracy: 0.9793
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 299 | 0.1478 | 0.9474 |
| 0.3935 | 2.0 | 598 | 0.0931 | 0.9662 |
| 0.3935 | 3.0 | 897 | 0.0853 | 0.9812 |
| 0.0556 | 4.0 | 1196 | 0.0918 | 0.9793 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "deberta-v3-base-rocstories", "results": []}]} | KeiHeityuu/deberta-v3-base-rocstories | null | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:57:30+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-base-rocstories
==========================
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0918
* Accuracy: 0.9793
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-cnn_v2_trained_on_250 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:59:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1420
- F1 Score: 0.7321
- Accuracy: 0.7321
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:--------:|
| 0.585 | 33.33 | 200 | 0.5491 | 0.7308 | 0.7315 |
| 0.4504 | 66.67 | 400 | 0.5979 | 0.7154 | 0.7154 |
| 0.4066 | 100.0 | 600 | 0.6367 | 0.7174 | 0.7174 |
| 0.365 | 133.33 | 800 | 0.6556 | 0.7193 | 0.7194 |
| 0.3371 | 166.67 | 1000 | 0.6928 | 0.7201 | 0.7201 |
| 0.316 | 200.0 | 1200 | 0.7166 | 0.7234 | 0.7234 |
| 0.3013 | 233.33 | 1400 | 0.7579 | 0.7204 | 0.7208 |
| 0.287 | 266.67 | 1600 | 0.7704 | 0.7228 | 0.7228 |
| 0.2714 | 300.0 | 1800 | 0.8006 | 0.7301 | 0.7301 |
| 0.2554 | 333.33 | 2000 | 0.8308 | 0.7280 | 0.7281 |
| 0.2392 | 366.67 | 2200 | 0.8802 | 0.7226 | 0.7228 |
| 0.2255 | 400.0 | 2400 | 0.8654 | 0.7250 | 0.7255 |
| 0.2089 | 433.33 | 2600 | 0.9485 | 0.7357 | 0.7361 |
| 0.1975 | 466.67 | 2800 | 0.8955 | 0.7254 | 0.7255 |
| 0.1865 | 500.0 | 3000 | 0.9397 | 0.7309 | 0.7315 |
| 0.1757 | 533.33 | 3200 | 0.9482 | 0.7261 | 0.7261 |
| 0.1679 | 566.67 | 3400 | 0.9625 | 0.7301 | 0.7301 |
| 0.1598 | 600.0 | 3600 | 0.9825 | 0.7307 | 0.7308 |
| 0.1523 | 633.33 | 3800 | 1.0183 | 0.7270 | 0.7275 |
| 0.1456 | 666.67 | 4000 | 1.0369 | 0.7346 | 0.7348 |
| 0.1383 | 700.0 | 4200 | 1.0198 | 0.7259 | 0.7261 |
| 0.134 | 733.33 | 4400 | 1.0428 | 0.7279 | 0.7281 |
| 0.1287 | 766.67 | 4600 | 1.0665 | 0.7298 | 0.7301 |
| 0.1251 | 800.0 | 4800 | 1.0236 | 0.7328 | 0.7328 |
| 0.1198 | 833.33 | 5000 | 1.0072 | 0.7368 | 0.7368 |
| 0.1176 | 866.67 | 5200 | 1.0620 | 0.7328 | 0.7328 |
| 0.1129 | 900.0 | 5400 | 1.1262 | 0.7306 | 0.7308 |
| 0.1092 | 933.33 | 5600 | 1.1331 | 0.7353 | 0.7355 |
| 0.1067 | 966.67 | 5800 | 1.1480 | 0.7304 | 0.7308 |
| 0.104 | 1000.0 | 6000 | 1.1257 | 0.7400 | 0.7401 |
| 0.1007 | 1033.33 | 6200 | 1.1232 | 0.7395 | 0.7395 |
| 0.0989 | 1066.67 | 6400 | 1.1229 | 0.7366 | 0.7368 |
| 0.0971 | 1100.0 | 6600 | 1.0789 | 0.7333 | 0.7335 |
| 0.0941 | 1133.33 | 6800 | 1.1300 | 0.7371 | 0.7375 |
| 0.0924 | 1166.67 | 7000 | 1.1574 | 0.7379 | 0.7381 |
| 0.0907 | 1200.0 | 7200 | 1.1460 | 0.7367 | 0.7368 |
| 0.0888 | 1233.33 | 7400 | 1.1449 | 0.7332 | 0.7335 |
| 0.089 | 1266.67 | 7600 | 1.1815 | 0.7350 | 0.7355 |
| 0.0862 | 1300.0 | 7800 | 1.1840 | 0.7381 | 0.7381 |
| 0.0855 | 1333.33 | 8000 | 1.1783 | 0.7319 | 0.7321 |
| 0.0846 | 1366.67 | 8200 | 1.1751 | 0.7387 | 0.7388 |
| 0.0825 | 1400.0 | 8400 | 1.2081 | 0.7370 | 0.7375 |
| 0.0819 | 1433.33 | 8600 | 1.2126 | 0.7345 | 0.7348 |
| 0.0812 | 1466.67 | 8800 | 1.2118 | 0.7297 | 0.7301 |
| 0.0807 | 1500.0 | 9000 | 1.1921 | 0.7319 | 0.7321 |
| 0.0809 | 1533.33 | 9200 | 1.1734 | 0.7360 | 0.7361 |
| 0.0794 | 1566.67 | 9400 | 1.2008 | 0.7327 | 0.7328 |
| 0.0787 | 1600.0 | 9600 | 1.2049 | 0.7319 | 0.7321 |
| 0.0796 | 1633.33 | 9800 | 1.1994 | 0.7312 | 0.7315 |
| 0.0786 | 1666.67 | 10000 | 1.1981 | 0.7319 | 0.7321 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_EMP_H3-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T08:00:46+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_EMP\_H3-seqsight\_16384\_512\_22M-L32\_all
===============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_EMP\_H3 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1420
* F1 Score: 0.7321
* Accuracy: 0.7321
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
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] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-pos
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:
- Loss: 0.1795
- Precision: 0.9386
- Recall: 0.9377
- F1: 0.9382
- Accuracy: 0.9579
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1872 | 1.0 | 1756 | 0.1998 | 0.9298 | 0.9300 | 0.9299 | 0.9520 |
| 0.1247 | 2.0 | 3512 | 0.1837 | 0.9357 | 0.9334 | 0.9345 | 0.9557 |
| 0.0913 | 3.0 | 5268 | 0.1795 | 0.9386 | 0.9377 | 0.9382 | 0.9579 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-pos", "results": []}]} | YanisGomes/bert-finetuned-pos | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:01:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-pos
==================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1795
* Precision: 0.9386
* Recall: 0.9377
* F1: 0.9382
* Accuracy: 0.9579
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
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] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
automatic-speech-recognition | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | mindvu/wav2vec2-xlsr-300m-malay | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:01:15+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Model Card Contact"
] | [
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_hh_shp4_dpo5
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7112
- Rewards/chosen: -9.5668
- Rewards/rejected: -9.8462
- Rewards/accuracies: 0.5400
- Rewards/margins: 0.2794
- Logps/rejected: -266.4585
- Logps/chosen: -255.0426
- Logits/rejected: -0.8301
- Logits/chosen: -0.8200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0505 | 2.67 | 100 | 1.9463 | -7.2393 | -6.9165 | 0.4500 | -0.3228 | -260.5990 | -250.3877 | -0.9466 | -0.9197 |
| 0.0379 | 5.33 | 200 | 3.2214 | -9.5352 | -9.9687 | 0.5 | 0.4335 | -266.7035 | -254.9795 | -0.9120 | -0.8842 |
| 0.0013 | 8.0 | 300 | 2.6505 | -10.4459 | -11.0078 | 0.5300 | 0.5620 | -268.7817 | -256.8008 | -0.7232 | -0.7080 |
| 0.0 | 10.67 | 400 | 2.7047 | -9.5022 | -9.7523 | 0.5300 | 0.2501 | -266.2706 | -254.9136 | -0.8290 | -0.8187 |
| 0.0 | 13.33 | 500 | 2.7070 | -9.5225 | -9.7846 | 0.5400 | 0.2621 | -266.3353 | -254.9541 | -0.8296 | -0.8194 |
| 0.0 | 16.0 | 600 | 2.7121 | -9.5623 | -9.8180 | 0.5400 | 0.2557 | -266.4020 | -255.0337 | -0.8298 | -0.8197 |
| 0.0 | 18.67 | 700 | 2.7255 | -9.5794 | -9.8090 | 0.5300 | 0.2296 | -266.3841 | -255.0679 | -0.8301 | -0.8200 |
| 0.0 | 21.33 | 800 | 2.7158 | -9.5789 | -9.8343 | 0.5300 | 0.2555 | -266.4347 | -255.0669 | -0.8298 | -0.8196 |
| 0.0 | 24.0 | 900 | 2.7341 | -9.5811 | -9.8243 | 0.5300 | 0.2432 | -266.4146 | -255.0712 | -0.8300 | -0.8197 |
| 0.0 | 26.67 | 1000 | 2.7112 | -9.5668 | -9.8462 | 0.5400 | 0.2794 | -266.4585 | -255.0426 | -0.8301 | -0.8200 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "model_hh_shp4_dpo5", "results": []}]} | guoyu-zhang/model_hh_shp4_dpo5 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-04-16T08:01:35+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
| model\_hh\_shp4\_dpo5
=====================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7112
* Rewards/chosen: -9.5668
* Rewards/rejected: -9.8462
* Rewards/accuracies: 0.5400
* Rewards/margins: 0.2794
* Logps/rejected: -266.4585
* Logps/chosen: -255.0426
* Logits/rejected: -0.8301
* Logits/chosen: -0.8200
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 1000
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.39.1
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chemical-ner-bert-large-uncased-3
This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0992 | 1.0 | 852 | 0.0537 |
| 0.0496 | 2.0 | 1704 | 0.0495 |
| 0.0333 | 3.0 | 2556 | 0.0524 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google-bert/bert-large-uncased", "model-index": [{"name": "chemical-ner-bert-large-uncased-3", "results": []}]} | shubhamgantayat/chemical-ner-bert-large-uncased-3 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-large-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:02:18+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-google-bert/bert-large-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chemical-ner-bert-large-uncased-3
=================================
This model is a fine-tuned version of google-bert/bert-large-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0524
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-CR-Cluster4di7-ConPre-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-CR-Cluster4di7-ConPre-3Epochs", "results": []}]} | tedad09/PolizzeDonut-CR-Cluster4di7-ConPre-3Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:04:30+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 #license-mit #endpoints_compatible #region-us
|
# PolizzeDonut-CR-Cluster4di7-ConPre-3Epochs
This model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
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"## Training procedure",
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bartpho-word-large-v3
This model is a fine-tuned version of [vinai/bartpho-word](https://huggingface.co/vinai/bartpho-word) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0601
- Bleu: 20.961
- Gen Len: 17.2576
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 2.0536 | 1.0 | 9155 | 2.0601 | 20.961 | 17.2576 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "vinai/bartpho-word", "model-index": [{"name": "bartpho-word-large-v3", "results": []}]} | long292/bartpho-word-large-v3 | null | [
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"base_model:vinai/bartpho-word",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:07:03+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-vinai/bartpho-word #autotrain_compatible #endpoints_compatible #region-us
| bartpho-word-large-v3
=====================
This model is a fine-tuned version of vinai/bartpho-word on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0601
* Bleu: 20.961
* Gen Len: 17.2576
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-pre-finetuned-cybersecurity_READMEs
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8817
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3461 | 1.0 | 11832 | 2.2546 |
| 2.1241 | 2.0 | 23664 | 2.0770 |
| 2.0181 | 3.0 | 35496 | 1.9866 |
| 1.9269 | 4.0 | 47328 | 1.9078 |
| 1.8556 | 5.0 | 59160 | 1.8970 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilroberta-base", "model-index": [{"name": "distilroberta-pre-finetuned-cybersecurity_READMEs", "results": []}]} | Marcell322/distilroberta-pre-finetuned-cybersecurity_READMEs | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:07:58+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-pre-finetuned-cybersecurity\_READMEs
==================================================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8817
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.40.0.dev0
* Pytorch 2.2.1
* Datasets 2.18.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0.dev0\n* Pytorch 2.2.1\n* Datasets 2.18.0\n* Tokenizers 0.15.1"
] |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# V0415MA1plus
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0713
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7976 | 0.09 | 10 | 0.1879 |
| 0.142 | 0.18 | 20 | 0.1048 |
| 0.0998 | 0.27 | 30 | 0.0795 |
| 0.0848 | 0.36 | 40 | 0.0713 |
| 0.0727 | 0.45 | 50 | 0.0728 |
| 0.0834 | 0.54 | 60 | 0.0712 |
| 0.0738 | 0.63 | 70 | 0.0660 |
| 0.0741 | 0.73 | 80 | 0.0674 |
| 0.0723 | 0.82 | 90 | 0.0675 |
| 0.0776 | 0.91 | 100 | 0.0679 |
| 0.0708 | 1.0 | 110 | 0.0669 |
| 0.0515 | 1.09 | 120 | 0.0636 |
| 0.0559 | 1.18 | 130 | 0.0680 |
| 0.0549 | 1.27 | 140 | 0.0672 |
| 0.0514 | 1.36 | 150 | 0.0601 |
| 0.059 | 1.45 | 160 | 0.0615 |
| 0.0494 | 1.54 | 170 | 0.0683 |
| 0.0555 | 1.63 | 180 | 0.0612 |
| 0.048 | 1.72 | 190 | 0.0601 |
| 0.058 | 1.81 | 200 | 0.0586 |
| 0.0491 | 1.9 | 210 | 0.0578 |
| 0.0423 | 1.99 | 220 | 0.0620 |
| 0.0243 | 2.08 | 230 | 0.0616 |
| 0.0238 | 2.18 | 240 | 0.0724 |
| 0.0207 | 2.27 | 250 | 0.0787 |
| 0.0203 | 2.36 | 260 | 0.0800 |
| 0.0238 | 2.45 | 270 | 0.0760 |
| 0.0216 | 2.54 | 280 | 0.0746 |
| 0.0214 | 2.63 | 290 | 0.0730 |
| 0.0246 | 2.72 | 300 | 0.0722 |
| 0.0246 | 2.81 | 310 | 0.0716 |
| 0.0237 | 2.9 | 320 | 0.0714 |
| 0.0267 | 2.99 | 330 | 0.0713 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0415MA1plus", "results": []}]} | Litzy619/V0415MA1plus | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-04-16T08:09:33+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
| V0415MA1plus
============
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0713
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 60
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 2.1.2+cu121
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] |
null | null | Fizural ট্যাবলেট কি?
Fizural দাম হল একটি বিপ্লবী হেমোরয়েড চিকিত্সা যা একটি শক্তিশালী সম্পূরক আকারে আবদ্ধ। তাদের প্রশান্তিদায়ক এবং নিরাময় বৈশিষ্ট্যগুলির জন্য বিখ্যাত প্রাকৃতিক উপাদানগুলির মিশ্রণে তৈরি, Fizural পর্যালোচনা ব্যথা, চুলকানি এবং প্রদাহ থেকে মুক্তি দেওয়ার জন্য হেমোরয়েডের মূল কারণগুলিকে লক্ষ্য করে। মৃদু কিন্তু কার্যকর হওয়ার জন্য ডিজাইন করা হয়েছে, Fizural উপাদান ঐতিহ্যগত হেমোরয়েড চিকিত্সার জন্য একটি অ-আক্রমণকারী বিকল্প অফার করে, যা ব্যক্তিদের তাদের দৈনন্দিন জীবনে স্বাচ্ছন্দ্য এবং আত্মবিশ্বাস পুনরুদ্ধার করতে দেয়।
সরকারী ওয়েবসাইট:<a href="https://www.nutritionsee.com/Fizurbangss">www.Fizural.com</a>
<p><a href="https://www.nutritionsee.com/Fizurbangss"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Fizural-Bangladesh.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/Fizurbangss">এখন কেন!! আরও তথ্যের জন্য নীচের লিঙ্কে ক্লিক করুন এবং এখনই 50% ছাড় পান... তাড়াতাড়ি করুন</a>
সরকারী ওয়েবসাইট:<a href="https://www.nutritionsee.com/Fizurbangss">www.Fizural.com</a> | {"license": "apache-2.0"} | FizuralBangladesh/FizuralBangladesh | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T08:09:57+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Fizural ট্যাবলেট কি?
Fizural দাম হল একটি বিপ্লবী হেমোরয়েড চিকিত্সা যা একটি শক্তিশালী সম্পূরক আকারে আবদ্ধ। তাদের প্রশান্তিদায়ক এবং নিরাময় বৈশিষ্ট্যগুলির জন্য বিখ্যাত প্রাকৃতিক উপাদানগুলির মিশ্রণে তৈরি, Fizural পর্যালোচনা ব্যথা, চুলকানি এবং প্রদাহ থেকে মুক্তি দেওয়ার জন্য হেমোরয়েডের মূল কারণগুলিকে লক্ষ্য করে। মৃদু কিন্তু কার্যকর হওয়ার জন্য ডিজাইন করা হয়েছে, Fizural উপাদান ঐতিহ্যগত হেমোরয়েড চিকিত্সার জন্য একটি অ-আক্রমণকারী বিকল্প অফার করে, যা ব্যক্তিদের তাদের দৈনন্দিন জীবনে স্বাচ্ছন্দ্য এবং আত্মবিশ্বাস পুনরুদ্ধার করতে দেয়।
সরকারী ওয়েবসাইট:<a href="URL
<p><a href="URL <img src="URL alt="enter image description here"> </a></p>
<a href="URL>এখন কেন!! আরও তথ্যের জন্য নীচের লিঙ্কে ক্লিক করুন এবং এখনই 50% ছাড় পান... তাড়াতাড়ি করুন</a>
সরকারী ওয়েবসাইট:<a href="URL | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# V0415MA2plus
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0731
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7881 | 0.09 | 10 | 0.1798 |
| 0.1395 | 0.18 | 20 | 0.1056 |
| 0.0994 | 0.27 | 30 | 0.0796 |
| 0.0852 | 0.36 | 40 | 0.0695 |
| 0.0718 | 0.45 | 50 | 0.0714 |
| 0.083 | 0.54 | 60 | 0.0731 |
| 0.0743 | 0.63 | 70 | 0.0669 |
| 0.0713 | 0.73 | 80 | 0.0658 |
| 0.0709 | 0.82 | 90 | 0.0618 |
| 0.074 | 0.91 | 100 | 0.0721 |
| 0.0697 | 1.0 | 110 | 0.0656 |
| 0.0515 | 1.09 | 120 | 0.0624 |
| 0.057 | 1.18 | 130 | 0.0756 |
| 0.0553 | 1.27 | 140 | 0.0623 |
| 0.0517 | 1.36 | 150 | 0.0667 |
| 0.0564 | 1.45 | 160 | 0.0582 |
| 0.0517 | 1.54 | 170 | 0.0671 |
| 0.0541 | 1.63 | 180 | 0.0586 |
| 0.0475 | 1.72 | 190 | 0.0587 |
| 0.0545 | 1.81 | 200 | 0.0582 |
| 0.0437 | 1.9 | 210 | 0.0627 |
| 0.0422 | 1.99 | 220 | 0.0654 |
| 0.0278 | 2.08 | 230 | 0.0590 |
| 0.0238 | 2.18 | 240 | 0.0699 |
| 0.0167 | 2.27 | 250 | 0.0802 |
| 0.0186 | 2.36 | 260 | 0.0847 |
| 0.024 | 2.45 | 270 | 0.0802 |
| 0.0213 | 2.54 | 280 | 0.0780 |
| 0.0217 | 2.63 | 290 | 0.0754 |
| 0.0237 | 2.72 | 300 | 0.0742 |
| 0.0235 | 2.81 | 310 | 0.0732 |
| 0.02 | 2.9 | 320 | 0.0732 |
| 0.0251 | 2.99 | 330 | 0.0731 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0415MA2plus", "results": []}]} | Litzy619/V0415MA2plus | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-04-16T08:11:32+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
| V0415MA2plus
============
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0731
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 60
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 2.1.2+cu121
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4ac-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7031
- F1 Score: 0.5998
- Accuracy: 0.6006
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6746 | 14.29 | 200 | 0.6661 | 0.5888 | 0.5982 |
| 0.6342 | 28.57 | 400 | 0.6705 | 0.5941 | 0.5994 |
| 0.6161 | 42.86 | 600 | 0.6796 | 0.5840 | 0.5836 |
| 0.5994 | 57.14 | 800 | 0.7018 | 0.5943 | 0.5982 |
| 0.5836 | 71.43 | 1000 | 0.6983 | 0.5891 | 0.5953 |
| 0.5716 | 85.71 | 1200 | 0.6989 | 0.5976 | 0.5985 |
| 0.5634 | 100.0 | 1400 | 0.7332 | 0.5860 | 0.5856 |
| 0.5559 | 114.29 | 1600 | 0.7191 | 0.5917 | 0.5915 |
| 0.5514 | 128.57 | 1800 | 0.7476 | 0.5880 | 0.5877 |
| 0.545 | 142.86 | 2000 | 0.7201 | 0.5870 | 0.5883 |
| 0.5402 | 157.14 | 2200 | 0.7254 | 0.5822 | 0.5824 |
| 0.535 | 171.43 | 2400 | 0.7615 | 0.5858 | 0.5853 |
| 0.5315 | 185.71 | 2600 | 0.7487 | 0.5863 | 0.5897 |
| 0.5256 | 200.0 | 2800 | 0.7467 | 0.5916 | 0.5962 |
| 0.5214 | 214.29 | 3000 | 0.7595 | 0.5885 | 0.5889 |
| 0.5171 | 228.57 | 3200 | 0.7499 | 0.5932 | 0.5933 |
| 0.5103 | 242.86 | 3400 | 0.7392 | 0.5930 | 0.5938 |
| 0.505 | 257.14 | 3600 | 0.7713 | 0.5948 | 0.5947 |
| 0.4987 | 271.43 | 3800 | 0.7678 | 0.5945 | 0.5947 |
| 0.4932 | 285.71 | 4000 | 0.7499 | 0.5918 | 0.5930 |
| 0.4864 | 300.0 | 4200 | 0.7599 | 0.5939 | 0.5941 |
| 0.4817 | 314.29 | 4400 | 0.7768 | 0.5897 | 0.5897 |
| 0.4753 | 328.57 | 4600 | 0.7994 | 0.5895 | 0.5921 |
| 0.4713 | 342.86 | 4800 | 0.7913 | 0.5919 | 0.5950 |
| 0.4684 | 357.14 | 5000 | 0.8242 | 0.5907 | 0.5924 |
| 0.4608 | 371.43 | 5200 | 0.8239 | 0.5885 | 0.5889 |
| 0.4568 | 385.71 | 5400 | 0.7942 | 0.5844 | 0.5842 |
| 0.4536 | 400.0 | 5600 | 0.8163 | 0.5833 | 0.5853 |
| 0.4498 | 414.29 | 5800 | 0.8163 | 0.5799 | 0.5795 |
| 0.446 | 428.57 | 6000 | 0.8405 | 0.5858 | 0.5880 |
| 0.4421 | 442.86 | 6200 | 0.8319 | 0.5844 | 0.5874 |
| 0.4386 | 457.14 | 6400 | 0.8389 | 0.5858 | 0.5886 |
| 0.4365 | 471.43 | 6600 | 0.8465 | 0.5793 | 0.5801 |
| 0.4323 | 485.71 | 6800 | 0.8422 | 0.5832 | 0.5862 |
| 0.4298 | 500.0 | 7000 | 0.8516 | 0.5788 | 0.5821 |
| 0.427 | 514.29 | 7200 | 0.8498 | 0.5786 | 0.5801 |
| 0.4242 | 528.57 | 7400 | 0.8509 | 0.5833 | 0.5856 |
| 0.4226 | 542.86 | 7600 | 0.8585 | 0.5802 | 0.5821 |
| 0.4204 | 557.14 | 7800 | 0.8566 | 0.5770 | 0.5774 |
| 0.4171 | 571.43 | 8000 | 0.8699 | 0.5781 | 0.5780 |
| 0.4149 | 585.71 | 8200 | 0.8636 | 0.5772 | 0.5786 |
| 0.4131 | 600.0 | 8400 | 0.8660 | 0.5803 | 0.5821 |
| 0.4106 | 614.29 | 8600 | 0.8705 | 0.5837 | 0.5848 |
| 0.4108 | 628.57 | 8800 | 0.8728 | 0.5831 | 0.5839 |
| 0.4104 | 642.86 | 9000 | 0.8719 | 0.5802 | 0.5818 |
| 0.407 | 657.14 | 9200 | 0.8794 | 0.5808 | 0.5818 |
| 0.4082 | 671.43 | 9400 | 0.8706 | 0.5815 | 0.5833 |
| 0.4061 | 685.71 | 9600 | 0.8767 | 0.5796 | 0.5804 |
| 0.4037 | 700.0 | 9800 | 0.8823 | 0.5820 | 0.5830 |
| 0.4053 | 714.29 | 10000 | 0.8768 | 0.5832 | 0.5842 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T08:12:12+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_EMP\_H4ac-seqsight\_16384\_512\_22M-L32\_all
=================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_EMP\_H4ac dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7031
* F1 Score: 0.5998
* Accuracy: 0.6006
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# T3qm7Strangemerges_32-7B
T3qm7Strangemerges_32-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [nlpguy/T3QM7](https://huggingface.co/nlpguy/T3QM7)
* [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: nlpguy/T3QM7
layer_range: [0, 32]
- model: Gille/StrangeMerges_32-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: nlpguy/T3QM7
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/T3qm7Strangemerges_32-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["nlpguy/T3QM7", "Gille/StrangeMerges_32-7B-slerp"]} | automerger/T3qm7Strangemerges_32-7B | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:nlpguy/T3QM7",
"base_model:Gille/StrangeMerges_32-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T08:12:49+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #automerger #base_model-nlpguy/T3QM7 #base_model-Gille/StrangeMerges_32-7B-slerp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T3qm7Strangemerges_32-7B
T3qm7Strangemerges_32-7B is an automated merge created by Maxime Labonne using the following configuration.
* nlpguy/T3QM7
* Gille/StrangeMerges_32-7B-slerp
## Configuration
## Usage
| [
"# T3qm7Strangemerges_32-7B\n\nT3qm7Strangemerges_32-7B is an automated merge created by Maxime Labonne using the following configuration.\n* nlpguy/T3QM7\n* Gille/StrangeMerges_32-7B-slerp",
"## Configuration",
"## Usage"
] | [
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"# T3qm7Strangemerges_32-7B\n\nT3qm7Strangemerges_32-7B is an automated merge created by Maxime Labonne using the following configuration.\n* nlpguy/T3QM7\n* Gille/StrangeMerges_32-7B-slerp",
"## Configuration",
"## Usage"
] |
null | transformers |
# DavidAU/hyperdrive-7b-alpha-Q8_0-GGUF
This model was converted to GGUF format from [`maldv/hyperdrive-7b-alpha`](https://huggingface.co/maldv/hyperdrive-7b-alpha) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/maldv/hyperdrive-7b-alpha) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/hyperdrive-7b-alpha-Q8_0-GGUF --model hyperdrive-7b-alpha.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/hyperdrive-7b-alpha-Q8_0-GGUF --model hyperdrive-7b-alpha.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m hyperdrive-7b-alpha.Q8_0.gguf -n 128
```
| {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["unsloth", "book", "llama-cpp", "gguf-my-repo"]} | DavidAU/hyperdrive-7b-alpha-Q8_0-GGUF | null | [
"transformers",
"gguf",
"unsloth",
"book",
"llama-cpp",
"gguf-my-repo",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:14:40+00:00 | [] | [] | TAGS
#transformers #gguf #unsloth #book #llama-cpp #gguf-my-repo #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# DavidAU/hyperdrive-7b-alpha-Q8_0-GGUF
This model was converted to GGUF format from 'maldv/hyperdrive-7b-alpha' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
| [
"# DavidAU/hyperdrive-7b-alpha-Q8_0-GGUF\nThis model was converted to GGUF format from 'maldv/hyperdrive-7b-alpha' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
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"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-CR-Cluster5di7-ConPre-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-CR-Cluster5di7-ConPre-3Epochs", "results": []}]} | tedad09/PolizzeDonut-CR-Cluster5di7-ConPre-3Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:15:01+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 #license-mit #endpoints_compatible #region-us
|
# PolizzeDonut-CR-Cluster5di7-ConPre-3Epochs
This model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# PolizzeDonut-CR-Cluster5di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
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] | [
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"# PolizzeDonut-CR-Cluster5di7-ConPre-3Epochs\n\nThis model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | adammoss/patch-pretrain-mask-5 | null | [
"transformers",
"safetensors",
"patchgpt",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:15:12+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #patchgpt #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## Model Card Contact"
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
* [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.1
layer_range: [0, 32]
- model: Open-Orca/Mistral-7B-OpenOrca
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.1
parameters:
t:
- filter: self_attn
value: 0.5
- filter: mlp
value: 0.5
- value: 0.5
dtype: bfloat16
``` | {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Open-Orca/Mistral-7B-OpenOrca", "mistralai/Mistral-7B-Instruct-v0.1"]} | jpquiroga/Mistral_7B_slerp_merge_instruct_open_orca | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T08:17:27+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-Open-Orca/Mistral-7B-OpenOrca #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Open-Orca/Mistral-7B-OpenOrca
* mistralai/Mistral-7B-Instruct-v0.1
### Configuration
The following YAML configuration was used to produce this model:
| [
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"### Merge Method\n\nThis model was merged using the SLERP merge method.",
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"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | kssumanth6/t5_small_chit_chat_generator_v1 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T08:17:29+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
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:
- Loss: 0.0600
- Precision: 0.9383
- Recall: 0.9520
- F1: 0.9451
- Accuracy: 0.9866
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0745 | 1.0 | 1756 | 0.0655 | 0.9089 | 0.9355 | 0.9220 | 0.9828 |
| 0.0347 | 2.0 | 3512 | 0.0585 | 0.9352 | 0.9492 | 0.9421 | 0.9863 |
| 0.0221 | 3.0 | 5268 | 0.0600 | 0.9383 | 0.9520 | 0.9451 | 0.9866 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | QMMMS/bert-finetuned-ner | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:17:49+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0600
* Precision: 0.9383
* Recall: 0.9520
* F1: 0.9451
* Accuracy: 0.9866
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ruBert-base-sberquad-0.01-len_4-filtered-negative-v2
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-sberquad-0.01-len_4-filtered-negative-v2", "results": []}]} | Shalazary/ruBert-base-sberquad-0.01-len_4-filtered-negative-v2 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T08:18:17+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us
|
# ruBert-base-sberquad-0.01-len_4-filtered-negative-v2
This model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# ruBert-base-sberquad-0.01-len_4-filtered-negative-v2\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 7000",
"### Training results",
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-cnn_v2_trained_on_500 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:18:55+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Compute Infrastructure",
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"## Model Card Contact"
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"## Training Details",
"### Training Data",
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"### Model Architecture and Objective",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-CR-Cluster6di7-ConPre-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-CR-Cluster6di7-ConPre-3Epochs", "results": []}]} | tedad09/PolizzeDonut-CR-Cluster6di7-ConPre-3Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:21:15+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 #license-mit #endpoints_compatible #region-us
|
# PolizzeDonut-CR-Cluster6di7-ConPre-3Epochs
This model is a fine-tuned version of tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
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] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-v0.1"} | cfli/mistral-m3-32-208 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-04-16T08:21:27+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.10.0 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.10.0"
] |
unconditional-image-generation | diffusers |
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('DangelTheMangel/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
| {"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]} | DangelTheMangel/sd-class-butterflies-32 | null | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-16T08:22:47+00:00 | [] | [] | TAGS
#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us
|
# Model Card for Unit 1 of the Diffusion Models Class
This model is a diffusion model for unconditional image generation of cute .
## Usage
| [
"# Model Card for Unit 1 of the Diffusion Models Class \n\nThis model is a diffusion model for unconditional image generation of cute .",
"## Usage"
] | [
"TAGS\n#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us \n",
"# Model Card for Unit 1 of the Diffusion Models Class \n\nThis model is a diffusion model for unconditional image generation of cute .",
"## Usage"
] |
text-generation | transformers | <p align="left">
<img src="https://huggingface.co/crimsonjoo/Neversleep-3B-Instruct-v0.1/resolve/main/neversleep_logo.webp" width="70%"/>
<p>
# "We must sleep, but AI Never Sleeps!"
## Prompt Template
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: {prompt}
Assistant:
```
## Simple-Usage
```python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-2.8B-v1.0", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("yanolja/EEVE-Korean-Instruct-2.8B-v1.0", trust_remote_code=True)
prompt_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {prompt}\nAssistant:\n"
text = '다이어트식 메뉴를 추천해주세요.\n\n(A) 샐러드\n(B) 치킨\n(C) 피자\n(D) 파스타'
model_inputs = tokenizer(prompt_template.format(prompt=text), return_tensors='pt')
outputs = model.generate(**model_inputs, max_new_tokens=256)
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(output_text)
```
### Example Output
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: 다이어트식 메뉴를 추천해주세요.
(A) 샐러드
(B) 치킨
(C) 피자
(D) 파스타
Assistant:
(A) 샐러드를 추천드립니다. 샐러드는 저칼로리이면서도 영양소가 풍부해 다이어트식으로 적합합니다. 다양한 채소와 단백질을 추가하여 균형 잡힌 식사를 만드실 수 있습니다.
```
## About the Model
First of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team!
This model is a fine-tuned version of [crimsonjoo/Neversleep-3B-v0.1](https://huggingface.co/crimsonjoo/Neversleep-3B-v0.1), which is a Korean vocabulary-extended version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2). Specifically, we utilized Direct Preference Optimization (DPO) through the use of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
For more details, please refer to our technical report: [Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models](https://arxiv.org/abs/2402.14714).
## Training Data
- Korean-translated version of [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
- Korean-translated version of [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
- No other dataset was used | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "yanolja/EEVE-Korean-2.8B-v1.0"} | crimsonjoo/Neversleep-3B-Instruct-v0.1 | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"conversational",
"custom_code",
"arxiv:2402.14714",
"base_model:yanolja/EEVE-Korean-2.8B-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T08:22:50+00:00 | [
"2402.14714"
] | [] | TAGS
#transformers #safetensors #phi #text-generation #generated_from_trainer #conversational #custom_code #arxiv-2402.14714 #base_model-yanolja/EEVE-Korean-2.8B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <p align="left">
<img src="URL width="70%"/>
<p>
# "We must sleep, but AI Never Sleeps!"
## Prompt Template
## Simple-Usage
### Example Output
## About the Model
First of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team!
This model is a fine-tuned version of crimsonjoo/Neversleep-3B-v0.1, which is a Korean vocabulary-extended version of microsoft/phi-2. Specifically, we utilized Direct Preference Optimization (DPO) through the use of Axolotl.
For more details, please refer to our technical report: Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.
## Training Data
- Korean-translated version of Open-Orca/SlimOrca-Dedup
- Korean-translated version of argilla/ultrafeedback-binarized-preferences-cleaned
- No other dataset was used | [
"# \"We must sleep, but AI Never Sleeps!\"\n ",
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"## Simple-Usage",
"### Example Output",
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"## Training Data\n - Korean-translated version of Open-Orca/SlimOrca-Dedup\n - Korean-translated version of argilla/ultrafeedback-binarized-preferences-cleaned\n - No other dataset was used"
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #generated_from_trainer #conversational #custom_code #arxiv-2402.14714 #base_model-yanolja/EEVE-Korean-2.8B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# \"We must sleep, but AI Never Sleeps!\"\n ",
"## Prompt Template",
"## Simple-Usage",
"### Example Output",
"## About the Model\n\nFirst of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team! \nThis model is a fine-tuned version of crimsonjoo/Neversleep-3B-v0.1, which is a Korean vocabulary-extended version of microsoft/phi-2. Specifically, we utilized Direct Preference Optimization (DPO) through the use of Axolotl.\n\nFor more details, please refer to our technical report: Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.",
"## Training Data\n - Korean-translated version of Open-Orca/SlimOrca-Dedup\n - Korean-translated version of argilla/ultrafeedback-binarized-preferences-cleaned\n - No other dataset was used"
] |
token-classification | transformers |
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, & Norwegian corpora (Top 5 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.56\% Accuracy)
## POS Tags
- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB | {"language": ["tl"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "pipeline_tag": "token-classification"} | iceman2434/xlm-roberta-base-ft-udpos213-top5lang | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"tl",
"dataset:universal_dependencies",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:23:52+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, & Norwegian corpora (Top 5 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.56\% Accuracy)
## POS Tags
- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB | [
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, & Norwegian corpora (Top 5 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.56\\% Accuracy)",
"## POS Tags\n- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, & Norwegian corpora (Top 5 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.56\\% Accuracy)",
"## POS Tags\n- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-xsum_v3_trained_on_10 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:27:55+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| {"library_name": "transformers", "tags": []} | relu-ntnu/bart-large-xsum_v3_trained_on_25 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T08:28:51+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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