stanford-crfm
/

BioMedLM

@@ -54,26 +54,21 @@ This model was a joint collaboration of [Stanford CRFM](https://crfm.stanford.ed
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
 It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. It should not be directly used for production or work that may directly impact people.
 ## Downstream Use
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 The main way we have used this model is finetuning for downstream question answering tasks, and we recommend using this model that way.
 ## Out-of-Scope Use
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.
 # Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 ## Recommendations
@@ -102,7 +97,6 @@ The model was trained on [MosaicML Cloud](https://www.mosaicml.com/cloud), a pla
 | betas | \[0.9, 0.95\]      |
 | weight decay    |  1.6e-5      |
 The training process was very smooth and did not suffer from any divergences.
 As we were preparing the training run, we were unsure of the benefits of training out to 300B tokens for language model perplexity and downstream task performance. While most models of this scale (e.g. GPT Neo 2.7B) are trained to 300-400B tokens, the datasets those models use are vastly larger than PubMed. For instance, The Pile is 8x the size of its PubMed subcorpora.
@@ -114,7 +108,6 @@ Fortunately, we did continue to see steady perplexity improvements on the valida
 The model uses a custom tokenizer trained on the PubMed Abstracts. When building domain specific models we have found it important to use a tokenizer trained on in-domain text to maximize performance on downstream tasks. A key benefit is that common biomedical terms are represented as entire tokens.
 For instance, all of these following terms are tokenized into single tokens by the biomedical tokenizer and multiple tokens by the standard GPT-2 tokenizer:
 |   |   |
 | --- | --- |

 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
 It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. It should not be directly used for production or work that may directly impact people.
 ## Downstream Use
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 The main way we have used this model is finetuning for downstream question answering tasks, and we recommend using this model that way.
 ## Out-of-Scope Use
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.
 # Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 ## Recommendations
 | betas | \[0.9, 0.95\]      |
 | weight decay    |  1.6e-5      |
 The training process was very smooth and did not suffer from any divergences.
 As we were preparing the training run, we were unsure of the benefits of training out to 300B tokens for language model perplexity and downstream task performance. While most models of this scale (e.g. GPT Neo 2.7B) are trained to 300-400B tokens, the datasets those models use are vastly larger than PubMed. For instance, The Pile is 8x the size of its PubMed subcorpora.
 The model uses a custom tokenizer trained on the PubMed Abstracts. When building domain specific models we have found it important to use a tokenizer trained on in-domain text to maximize performance on downstream tasks. A key benefit is that common biomedical terms are represented as entire tokens.
 For instance, all of these following terms are tokenized into single tokens by the biomedical tokenizer and multiple tokens by the standard GPT-2 tokenizer:
 |   |   |
 | --- | --- |