Initial Query

What do I do to fix AutoModel.from_pretrained to make it auto model with lm head

LLM Generation

Query

What do I do to fix AutoModel.from_pretrained to make it auto model with lm head

Output

When using the `AutoModel.from_pretrained` method from the Hugging Face Transformers library, it loads a base model without a language model (LM) head. If you want to load a model with an LM head (e.g., for tasks like text generation or masked language modeling), you should use `AutoModelForCausalLM`, `AutoModelForMaskedLM`, or another appropriate class depending on the specific task. Here’s how you can fix it: ### 1. **For Causal Language Modeling (e.g., GPT-like models):** Use `AutoModelForCausalLM` for models that generate text sequentially (e.g., GPT-2, GPT-3). 

1
2
3
4
   from transformers import AutoModelForCausalLM

   model = AutoModelForCausalLM.from_pretrained("gpt2")
### 2. **For Masked Language Modeling (e.g., BERT-like models):** Use `AutoModelForMaskedLM` for models that predict masked tokens (e.g., BERT, RoBERTa). 
1
2
3
4
   from transformers import AutoModelForMaskedLM

   model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")
### 3. **For Sequence Classification:** Use `AutoModelForSequenceClassification` for tasks like sentiment analysis. 
1
2
3
4
   from transformers import AutoModelForSequenceClassification

   model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
### 4. **For Question Answering:** Use `AutoModelForQuestionAnswering` for tasks like SQuAD. 
1
2
3
4
   from transformers import AutoModelForQuestionAnswering

   model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased")
### 5. **For Token Classification (e.g., Named Entity Recognition):** Use `AutoModelForTokenClassification` for tasks like NER. 
1
2
3
4
   from transformers import AutoModelForTokenClassification

   model = AutoModelForTokenClassification.from_pretrained("bert-base-uncased")
### 6. **For Seq2Seq Tasks (e.g., Translation, Summarization):** Use `AutoModelForSeq2SeqLM` for sequence-to-sequence models (e.g., T5, BART). 
1
2
3
4
   from transformers import AutoModelForSeq2SeqLM

   model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
### Summary: - Use `AutoModelForCausalLM` for causal language modeling. - Use `AutoModelForMaskedLM` for masked language modeling. - Use other task-specific `AutoModelFor*` classes depending on your use case. ### Example: If you were previously using: 
1
2
3
from transformers import AutoModel

model = AutoModel.from_pretrained("gpt2")
And you want a model with an LM head for text generation, change it to: 
1
2
3
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("gpt2")
This will load the model with the appropriate head for your task.

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: True